Research Engineer Jobs

About the Team OpenAI develops models that can reason through complex problems and hardware designed for the demands of advanced AI. AI for Chips connects these efforts: applying increasingly capable AI systems to the work of semiconductor engineering. Our goal is to help engineers develop better chips and shorten design cycles. This work brings research, model training, and hardware expertise together to build tools that engineers can use on real designs, with correctness and measurable performance at the center. About the Role We’re hiring a Research Engineer to help OpenAI models solve chip-design problems through reinforcement learning, tool use, and evaluation. You’ll own experiments from the initial idea through implementation and analysis. That means building environments and evaluations, running training, investigating failures, and using the results to decide what to try next. You’ll also build the software needed to make those experiments reliable and reproducible. We value strong coding fundamentals, careful experimental judgment, and the ability to make progress independently. Prior chip-design experience is helpful, but you can learn the domain alongside the team’s hardware specialists. In this role, you will: Build RL environments and evaluations for tasks such as RTL generation, design verification, and physical design optimization. Develop and test approaches that help models use chip-design tools and improve power, performance, and area while preserving correctness. Design experiments, establish baselines, and measure whether improvements hold up on new tasks and designs. Investigate failures across model behavior, rewards, evaluation tools, and experiment infrastructure. Improve iteration speed through better tooling, faster evaluations, and proxy rewards that reflect the outcomes we care about. Turn successful experiments into reusable research code and training workflows, working closely with researchers and engineers. You might thrive in this role if you: Have strong programming and debugging skills and a track record of turning technical ideas into working software. Experience with reinforcement learning, model evaluations, post-training, or other applied ML research. Experience building tool-using agents, reward functions, or automated evaluation systems. Can form clear hypotheses, design useful experiments, and distinguish meaningful results from noise or evaluation errors. Work independently on ambiguous problems and make practical decisions about what to build or test next. Stay close to the implementation and can explain what you built, what failed, and what you learned. Communicate progress clearly and collaborate well with people across research, software, and hardware. Care about developing safe, beneficial AI. Nice to have: Familiarity with experiment orchestration, distributed training, or research infrastructure. Experience with RTL, Verilog/SystemVerilog, EDA tools, formal verification, or chip-design automation. To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. Excellence everywhere: Everything we do should match the quality of our AI models. Global team: We prioritize your talent, not your location. What we offer Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. Learning & development : ElevenLabs proactively supports professional development through an annual discretionary stipend. Social travel : We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. Co-working : If you’re not located near one of our main hubs, we offer a monthly co-working stipend. About the role We are looking for a Research Engineer to join the research team at ElevenLabs, focused on the data infrastructure that powers our frontier AI models. The quality of our models is bounded by the quality and scale of the data behind them, and you will own the systems that make world-class data possible. You will thrive in this role if you enjoy: Building large-scale data pipelines for collecting, processing, filtering, and transforming datasets used to train state-of-the-art models. Training models used in our data processing pipelines, such as classifiers, quality filters, and labeling models. Designing data curation strategies such as deduplication, quality scoring, labeling, and augmentation that measurably improve model performance. Creating tooling and infrastructure that lets researchers explore and train on massive datasets quickly and reliably. Requirements We do not require any formal certifications or degrees. Instead, we are seeking enthusiastic engineers who can showcase solving impressively hard problems with artifacts such as past projects, designs, or GitHub contributions. Ideally, you bring: Experience building data-intensive systems, ideally in support of machine learning training pipelines. Strong engineering skills in distributed data processing at scale (e.g., Kubernetes, or custom pipelines over large datasets). The capacity to autonomously evaluate how data quality, composition, and curation affect model outcomes, and to build the tooling to measure it. Bonus: Experience building or operating web crawlers. Location This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw. #LI-Remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. Excellence everywhere: Everything we do should match the quality of our AI models. Global team: We prioritize your talent, not your location. What we offer Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. Learning & development : ElevenLabs proactively supports professional development through an annual discretionary stipend. Social travel : We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. Co-working : If you’re not located near one of our main hubs, we offer a monthly co-working stipend. About the role We are looking for a Research Engineer to join the research team at ElevenLabs, focused on large-scale web crawling for our frontier AI models. The quality of our models is bounded by the quality and scale of the data behind them, and you will own the crawling systems that source world-class data from the open web. You will thrive in this role if you enjoy: Building and operating large-scale, distributed web crawlers that discover, fetch, and extract data across billions of pages reliably and efficiently. Solving hard crawling problems such as content extraction from messy HTML, deduplication at web scale, freshness and recrawl strategies, and politeness and rate-limit handling. Designing targeted crawling pipelines that find high-value data sources, including audio, video, and multilingual content, and turn them into clean training-ready datasets. Creating tooling and infrastructure that lets researchers request, monitor, and explore newly crawled web data quickly and reliably. Requirements We do not require any formal certifications or degrees. Instead, we are seeking enthusiastic engineers who can showcase solving impressively hard problems with artifacts such as past projects, designs, or GitHub contributions. Ideally, you bring: Hands-on experience building and scaling web crawlers or scraping systems, ideally in support of machine learning training data. Strong engineering skills in distributed systems at scale (e.g., Kubernetes, queue-based architectures, or custom pipelines processing billions of documents). The capacity to autonomously evaluate the quality, coverage, and compliance of crawled data, and to build the tooling to measure it. Location This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw. #LI-Remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. Excellence everywhere: Everything we do should match the quality of our AI models. Global team: We prioritize your talent, not your location. What we offer Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. Learning & development : ElevenLabs proactively supports professional development through an annual discretionary stipend. Social travel : We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. Co-working : If you’re not located near one of our main hubs, we offer a monthly co-working stipend. About the role We are looking for a Research Engineer to join the research team at ElevenLabs, focused on deploying and optimizing our frontier AI models in production. The quality of our models only matters if they can be served fast, reliably, and at scale. You will own the systems that turn research breakthroughs into real-time products used by millions. You will thrive in this role if you enjoy: Deploying state-of-the-art models to production and owning the path from research checkpoint to serving infrastructure. Optimizing inference performance across the stack, including latency, throughput, and cost, using techniques such as quantization, distillation, KV-cache optimization, batching strategies, and custom kernels. Building and tuning high-performance serving systems for real-time, streaming workloads where every millisecond matters. Creating tooling and infrastructure that lets researchers ship new models to production quickly, safely, and with confidence in their performance characteristics. Requirements We do not require any formal certifications or degrees. Instead, we are seeking enthusiastic engineers who can showcase solving impressively hard problems with artifacts such as past projects, designs, or GitHub contributions. Ideally, you bring: Experience deploying and serving ML models in production, ideally for latency-sensitive or real-time applications. Strong engineering skills in GPU programming and inference optimization (e.g., CUDA, Triton, TensorRT, or serving frameworks such as vLLM or SGLang). The capacity to autonomously profile, diagnose, and eliminate bottlenecks across the serving stack, from model architecture to kernels to orchestration, and to build the tooling to measure it. Location This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw. #LI-Remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. Excellence everywhere: Everything we do should match the quality of our AI models. Global team: We prioritize your talent, not your location. What we offer Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. Learning & development : ElevenLabs proactively supports professional development through an annual discretionary stipend. Social travel : We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. Co-working : If you’re not located near one of our main hubs, we offer a monthly co-working stipend. About the role We are looking for a Research Engineer to join the research team at ElevenLabs, focused on the data infrastructure that powers our frontier AI models. The quality of our models is bounded by the quality and scale of the data behind them, and you will own the systems that make world-class data possible. You will thrive in this role if you enjoy: Building large-scale data pipelines for collecting, processing, filtering, and transforming datasets used to train state-of-the-art models. Training models used in our data processing pipelines, such as classifiers, quality filters, and labeling models. Designing data curation strategies such as deduplication, quality scoring, labeling, and augmentation that measurably improve model performance. Creating tooling and infrastructure that lets researchers explore and train on massive datasets quickly and reliably. Requirements We do not require any formal certifications or degrees. Instead, we are seeking enthusiastic engineers who can showcase solving impressively hard problems with artifacts such as past projects, designs, or GitHub contributions. Ideally, you bring: Experience building data-intensive systems, ideally in support of machine learning training pipelines. Strong engineering skills in distributed data processing at scale (e.g., Kubernetes, or custom pipelines over large datasets). The capacity to autonomously evaluate how data quality, composition, and curation affect model outcomes, and to build the tooling to measure it. Bonus: Experience building or operating web crawlers. Location This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw. #LI-Remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. About the role As an Applied Research Engineer in our Video team, you will help build the next generation of production-grade foundation models for human-centric video generation. You will join a highly focused team working at the intersection of large-scale generative modeling, distributed systems, and production engineering. Our mission is to develop and optimize video base models that power realistic, controllable, and emotionally expressive synthetic humans at scale. This is not pure research. This is applied research with direct product impact. You will work on advancing training recipes, scaling distributed systems, improving evaluation frameworks, and optimizing inference to ensure our models are high quality, stable, and efficient enough for real-world deployment. Your work will directly influence models used by tens of thousands of businesses worldwide. What you’ll do You will own and execute end-to-end research and engineering projects, from hypothesis to production impact. This includes: Developing and scaling latent video diffusion models tailored for human-centric video generation Designing conditioning mechanisms to improve control (pose, emotion, script, camera) without sacrificing fidelity Advancing distributed training strategies (DDP, FSDP, DeepSpeed, sequence parallelism) under real compute constraints Improving training stability at multi-node scale Designing rigorous evaluation frameworks combining automated metrics and structured human evaluation Optimizing inference for low latency, high resolution, and cost efficiency Running controlled ablations and experiments to drive high-signal modeling decisions Contributing to high engineering standards: reproducibility, experiment tracking, CI/CD, monitoring You will be expected to move fast, run multiple hypotheses in parallel, identify signal early, and focus on outcomes rather than exploration for its own sake. What we’re looking for Must-have Strong experience training deep learning models at scale Strong Python and PyTorch skills Hands-on experience with diffusion models (image domain required; video preferred) Experience with large scale multi-GPU / multi-node training Good understanding of distributed training (DDP, FSDP, DeepSpeed or similar) Ability to design controlled experiments and interpret noisy results Nice-to-have Experience with video diffusion models Experience in avatar or human-centric generation Familiarity with world / interactive models Experience with GANs or VAEs Experience optimizing inference systems for production Our stack Python, PyTorch, CUDA DeepSpeed, distributed training & inference Sequence parallelism AWS, SLURM, Docker GitHub, CI/CD pipelines Who you are You are research-driven but outcome-focused You care about shipping, not just publishing You can explore multiple ideas quickly and drop low-signal directions early You communicate clearly and present results scientifically You operate independently but collaborate actively across teams Why join us? Build production-scale video foundation models in a fast-growing Generative AI company Work on human-centric video generation with real-world impact Tackle hard problems in scaling, stability, and controllability Influence the direction of next-generation synthetic human technology Join a highly technical, high-ownership environment where your work ships If you want to work on cutting-edge generative video models and see your research power real-world products, we’d love to talk. Our culture At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here. Serving 50,000+ customers (and 50% of the Fortune 500) We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2 . Proprietary AI technology Since 2017, we’ve been pioneering advancements in Generative AI. Our AI technology is built in-house, by a team of world-class AI researchers and engineers. Learn more about our AI Research Lab and the team behind. AI Safety, Ethics and Security AI safety, ethics, and security are fundamental to our mission. While the full scope of Artificial Intelligence's impact on our society is still unfolding, our position is clear: People first. Always. Learn more about our commitments to AI Ethics, Safety & Security .

Research Engineer, Interactive World Models - New College Grad 2026 — CA, Santa Clara. Apply via Workday.

Research Engineer, Interactive World Models — CA, Santa Clara. Apply via Workday.

Senior Research Engineer, Interactive World Models — CA, Santa Clara. Apply via Workday.

Salary range - $250k - $350k | Equity - up to 0.5% | In-person NYC About Datalab Datalab trains models that read documents reliably at scale. The world's most important information is trapped in PDFs, scans, and files that can't easily be parsed, and getting it out correctly matters. From frontier AI labs processing training data to Fortune 500s like Siemens extracting decades of engineering records, Datalab is where businesses turn to when extraction has to be right. We’re at an 8-figure run rate with a team of 7. Anthropic is a customer. And we have hundreds more across FAANG, frontier AI labs, healthcare, finance, government, and legal. Our tools, Chandra, Surya, Marker, and Lift, have 70,000+ GitHub stars and broad developer mindshare. We're backed by founding members of OpenAI, FAIR, and Hugging Face. Role Overview We're looking for a Research Engineer to own problems end to end across our models, inference service, and product. You won't just train a model and hand it off. You'll take it from training through benchmarking, into our inference stack, and work with the team to integrate it into our products. We're a small team that has shipped the current state of the art OCR model, Chandra. Our models collectively have 70k+ Github stars. Our tools are used internally at frontier AI labs like Anthropic, and Fortune 500 enterprises like Siemens. Our team focuses on training small, efficient models that outperform much larger LLMs on domain-specific tasks (like OCR, structured extraction, tables). We move fast, prioritize practical results, and build tools that are open, reproducible, and built to last. You'll test hypotheses quickly, iterate on results, and balance experimental rigor with shipping to customers. Day to day: A typical project might look like: identify a gap in extraction quality on long documents, train and benchmark a new model, optimize it for inference, and work with the team to ship it to users. Concretely: Train and evaluate models: Train task-specific models (OCR, layout, text recognition, extraction). Explore architectures and training strategies to optimize task performance. This includes our open source models, like Marker, Surya, and Chandra. Optimize inference: Profile and accelerate model inference across different hardware setups (H100s, B200s, L40s, CPUs). Ship to product: Work with the team to integrate models into our API and product, helping define how new capabilities surface for end users. You will be involved from model training through integration, although your work will be weighted much more towards the model side than the product side. Create and maintain datasets: Source, design, and clean datasets for supervised and synthetic training; create reproducible pipelines for data versioning and evaluation. Experiment and benchmark: Run ablations, track metrics, and publish findings that inform model design and internal research direction. Engage with users and partners: Occasionally join calls or Slack threads to better understand customer needs and inform your work. Ideal Candidate You've shipped models that made it into production. You understand how to balance exploration with delivery, and how to turn research insights into products people actually use. 3+ years experience training, fine-tuning, and evaluating deep learning models Trained at least one production-grade model or system used in real-world applications Deep expertise in PyTorch and Python, with strong fundamentals in deep learning (optimization, evaluation, architecture design) Comfortable with data engineering, benchmarking, and performance profiling across hardware setups Comfortable with an early stage startup - balance running ablations/benchmarks with shipping velocity Bonus points if you: Have experience with OCR, document AI, or structured extraction Have published work, whether that's a paper, a benchmark report, or a deep technical blog post Have been a major contributor to open-source projects, especially in ML, vision, or NLP Enjoy writing about your work and sharing learnings with the community Interview process A 30-minute video call to evaluate fit 90-minute live architecture discussion Culture fit interview/team meeting At this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly. We can’t wait to hear from you! Apply here with your resume and references to past work to be considered.

AI Research Engineer/Scientist — US, California, Santa Clara. Apply via Workday.

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. Our research spans: Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science Imaging foundation models trained across the world's largest microscopy datasets Reasoning and agentic systems that connect frontier LLMs with biological foundation models Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled Join Our Team! As a Research Engineer, you'll build the models and systems that define what AI can do in biology: foundation models, reasoning, reinforcement learning, and multi-agent systems at frontier scale. What You'll Do Build on and advance the AI systems at the frontier of biology to accelerate science Design novel model architectures and scale pre-training pipelines for our biology foundation models. Build post-training systems that drive biological discovery: RL, reward modeling, reasoning, and multi-agent orchestration for long-horizon scientific tasks. Design evaluation frameworks and AI systems with scientists across Biohub and beyond, grounded in real biological outcomes and real world impact. Engage the wider scientific community through impactful publications, open-source releases, and collaborations worldwide. What You'll Bring Significant experience building and scaling deep learning systems, ideally in research-driven environments Strong engineering and scientific fundamentals Comfort with ambiguity, rapid iteration, and loosely-defined research problems Strong communication skills across technical and scientific audiences Compensation The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $214,000 to $375,000 annually. Final compensation is based on the level at which you are hired. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. Benefits for the Whole You We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. Provides a generous employer match on employee 401(k) contributions to support planning for the future. Paid time off to volunteer at an organization of your choice. Funding for select family-forming benefits. Relocation support for employees who need assistance moving Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! #LI-Hybrid

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. Our research spans: Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science Imaging foundation models trained across the world's largest microscopy datasets Reasoning and agentic systems that connect frontier LLMs with biological foundation models Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled Join Our Team! As a Research Engineer, you'll build the models and systems that define what AI can do in biology: foundation models, reasoning, reinforcement learning, and multi-agent systems at frontier scale. What You'll Do Build on and advance the AI systems at the frontier of biology to accelerate science Design novel model architectures and scale pre-training pipelines for our biology foundation models. Build post-training systems that drive biological discovery: RL, reward modeling, reasoning, and multi-agent orchestration for long-horizon scientific tasks. Design evaluation frameworks and AI systems with scientists across Biohub and beyond, grounded in real biological outcomes and real world impact. Engage the wider scientific community through impactful publications, open-source releases, and collaborations worldwide. What You'll Bring Significant experience building and scaling deep learning systems, ideally in research-driven environments Strong engineering and scientific fundamentals Comfort with ambiguity, rapid iteration, and loosely-defined research problems Strong communication skills across technical and scientific audiences Compensation The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $214,000 to $375,000 annually. Final compensation is based on the level at which you are hired. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. Benefits for the Whole You We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. Provides a generous employer match on employee 401(k) contributions to support planning for the future. Paid time off to volunteer at an organization of your choice. Funding for select family-forming benefits. Relocation support for employees who need assistance moving Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! #LI-Hybrid

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. We are looking for an engineer with strong experience in machine learning and solid foundations in maths and computer science to join our growing Post-Training team at Baseten. Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer’s specific needs. Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often if involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels. RECENT RESEARCH Dense, on-policy or both? Repeated kv cache for long-running agents Distillation without the dark – replicating black-box on-policy distillation on Baseten We don’t have a rigid set of skills, but here’s some of what we’re looking for: A deep understanding of modern ML techniques and tools for training transformers Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library The ability to perform roofline analysis on a transformer training setup A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerisation, and networking protocols A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices BENEFITS Competitive compensation, including meaningful equity. 100% coverage of medical, dental, and vision insurance for employee and dependents Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) Paid parental leave Fertility and family-building stipend through Carrot Company-facilitated 401(k) Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. The opportunity At Synthesia we really care about video generation, especially about human centric avatar video generation. This led us to release models such as EXPRESS-Video , and soon our latest video model - these are the best avatar video models in the world, and we are committed to continuing and double down our efforts in leading that area. Our goal is to get to human centric video models that can generate arbitrary long videos at high resolution with arbitrary actions and events. That means continuously training large generative video models from scratch with the proprietary data pipelines and compute infrastructure to support it at scale. We are looking for a technical leader who owns the full stack end-to-end, someone who bridges pre-training and post-training, sets long-term direction alongside research leadership, and is personally present at the hardest parts of the work. If building foundation model capability from the ground up at a company genuinely committed to leading the field sounds like the right next challenge, this role was written for you. About the role Synthesia's video generation capability is core to everything we ship. It involves roughly 15 people working daily across pre-training and post-training stages, and the complexity of coordinating across those stages, at the scale of compute and data we now operate, requires a different kind of technical leadership. We're looking for a Principal Research Engineer (L7) to own the full technical stack for offline video generation. This is a senior individual contributor position with outsized scope and influence. You'll partner directly with research leadership and team leads to define long-term strategy, resolve the hardest cross-cutting technical problems, and raise the bar for how quickly research reaches product. The person we're looking for has trained large generative models from scratch, not supervised it from a distance, but done it, debugged it, and shipped it. More than that, they're driven by a genuine ambition to push what's possible in video generation, and they care deeply about seeing that work land in product and reach users. What you'll do Own the end-to-end technical direction for offline video generation, spanning pre-training and post-training, resolving the artificial boundary between those two stages in service of shipping better models faster. Partner with research leadership and team leads to define a unified long-term roadmap, broken into achievable objectives, and drive execution against it. Identify the most critical technical gaps across the video generation pipeline and jump in to unblock them, whether that means architectural decisions, training stability, post-training alignment, or cross-team coordination. Increase the velocity at which research ships to product: accelerate problem-solving, improve research-to-production handoffs, and increase visibility of research output in partnership with PMs. Coach and elevate more junior researchers and engineers toward senior technical thinking and execution. Help shape team structure and refine processes to enable high-velocity, cohesive execution across research. You'll thrive in this role if you have A proven track record training large-scale video generation models from scratch, across multiple nodes, at the scale of millions of hours of data. Deep experience with post-training techniques at scale: RLHF, GRPO, DPO, and the judgment to know when and how to apply them. A proven track record data quality and you are not reluctant to question it. The ability to think strategically about multi-year research direction and execute hands-on at the frontier of what the team is building. Strong cross-functional influence: you shape how teams work together without needing positional authority to do it. A leadership style grounded in technical involvement. You lead by example, inspire through craft, and communicate with clarity. Genuine hunger to unlock new capabilities and an obsession with shipping. You're not satisfied by research that stays in a notebook. You want it in the hands of users. Particularly relevant experience Having owned a major model generation or capability jump end-to-end, from training runs through to product deployment. Working across both pre-training and post-training stages on the same model family, with direct accountability for the outcomes of both. Experience operating at scale: large distributed training runs, significant compute budgets, and multi-million hour data pipelines. Applying alignment and fine-tuning techniques in a video or multimodal context, not just text. Experience with human feedback pipelines applied to generative video or audio. Leading or significantly influencing the technical direction of a research team while remaining hands-on. Dealbreakers We will not be a good fit if you prefer to lead without staying technically involved, or if clear and direct communication across research and product isn't one of your strengths. This role requires presence at the frontier of the work, not above it. And if shipping doesn't excite you as much as the research itself, this probably isn't the right role.