Research Engineer In Data Jobs

About the Team The Personal AGI team is responsible for training and improving pre-trained models to be deployed into ChatGPT, the API, and potential future products. In the Model Experience team, we shape the default character and behavior of ChatGPT: how the model communicates, responds to users, uses its capabilities, and behaves across different contexts and languages. Our goal is to make every interaction with ChatGPT thoughtful, helpful, and trustworthy. We take an opinionated view of what good human–AI interaction should look like, then turn that vision into real model behavior through human data, evaluations, reward models, and post-training. Our work sits at the intersection of research, product, and model design. We partner closely with teams across OpenAI to conduct research and ensure our models are thoughtful, safe, reliable to serve millions of users. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research and the quality of human-AI interaction. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve model capability and performance. Collaborate closely with the other research and product teams, allowing customers to optimize their own models. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have good judgment about model behavior and can communicate this judgment effec

Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are looking for a Lead AI Research Engineer, Embodied Systems to lead and own the engineering of the systems that turn embodied AI into real-world robot behavior. You will be the technical owner and lead for the full embodied systems stack — the onboard AI system, the data collection system, the teleoperation systems, and the on-robot reinforcement learning system — driving the direction hands-on and closing the loop between data, models, and action in the physical world. Responsibilities Lead and own, from the engineering side, the embodied systems that power RoboForce's data flywheel — the onboard AI (inference) system, the data collection system, the teleoperation systems and the on-robot reinforcement learning system. Set the technical direction and architecture for how learned models run, are evaluated, and improve on real robots. Deliver these systems end-to-end on physical robots — from bring-up through reliable, real-time operation in demanding industrial environments. Own on-robot deployment and closed-loop evaluation of policies, turning real-world performance into measurable improvements. Partner with and influence the robotics software team and the ML research team to align interfaces and priorities across the stack. Grow the direction — mentor engineers and raise the technical bar for embodied systems work. Requirements Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, or related field with significant relevant experience, or a PhD degree. Track record of leading complex robotic or embodied systems end-to-end and setting technical direction for other engineers. Strong proficiency in both C++ and Python, with solid systems programming and real

About the Team The Proactivity Research team, within OpenAI’s broader Personal AGI team, is focused on making our models in ChatGPT and future potential products proactive in ways that are truly useful. We're laying the technical foundations for AI that can anticipate what users need in real time, adapt as their goals and preferences shift, and build a deeper, evolving understanding of the person it's helping. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models’ personalization and agentic capabilities. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a highly personalized, collaborative, and proactive assistant. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve the proactivity and ability of our models to further user goals. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. Collaborate closely with the other research and product teams to influence the shape of technical solutions in the product You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have a working knowledge of LLM post-training and evaluation approaches Are passionate about, or have experience thinking about, personalization and enabling users to achieve their goals Are comfortable diving into a large ML codebase to debug. Thrive in a dynamic and technically complex environment. About OpenAI

About the Team The Personal AGI team seeks to empower all of humanity to benefit from frontier intelligence in whatever way they choose. We are responsible for training models to deploy to millions of users globally via ChatGPT, the API, and future products. We aim to evolve ChatGPT from a chatbot to an infinitely capable and personalized superassistant supporting human flourishing. We work on defining, measuring, and improving capabilities across the training stack. Our focus areas include but are not limited to model behavior, personalization, safety, factuality, instruction following, personality, interactivity, multilingual fluency, world interaction, and bringing agents to everyone. We chart the course for what to strive towards. We partner closely with research and product teams across the company ensuring that our models are safe, efficient, and reliable. About the Role You’ll work as a Research Engineer / Scientist on the North Stars team within the broader Personal AGI research org. You will work on bringing the next generation of AI-enabled experiences to all of humanity by closing the capability overhang between power users and the average consumer, including areas like tool-use, feature discovery, connectors, and instruction following. You will think deeply about the current bottlenecks in model behavior, translate these insights into robust evals, training data, reward signals, and model and harness improvements. We're looking for individuals with strong ML engineering skills and research experience passionate about creative, product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve model capability and performance. Collaborate closely with the other research and product teams, allowing customers to optimize their own models. Build robust evaluations for tracking modelin

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. The Opportunity Adyen is building a top-tier AI engineering organization in Amsterdam, San Francisco and Madrid to drive our next chapter of innovation using AI globally across the entire company. This is a highly technical, hands-on role focused on the exploration and application of cutting-edge AI research within the financial technology sector. As a Senior AI Research Engineer, you will operate with a high degree of autonomy and responsibility , delivering strategic, high-impact outcomes that bridge the gap between advanced AI research and production-grade applications at a global scale, potentially impacting trillions of dollars in transactions annually. What You'll Do: Innovate and Deploy: Drive the execution of Adyen's AI strategy , focusing on the practical application of Generative AI (GenAI) and other AI methodologies in finance . This includes contributing to Adyen's efforts in key research areas such as AI agents for data analysis and operational workflows , human-in-the-loop for integrity risk , and development of foundation models . For instance, you might contribute to initiatives like the Data Agent Benchmark for Multi-step Reasoning (DABStep) , which evaluates AI agents on real-world data analysis tasks, including those from the financial sector. Build Production-grade Applications: Bridge the gap between cutti

The Research Engineer – Mechatronics is a hands-on engineering role within the TaylorMade Research & Development team, focused on the design, prototyping, integration, and validation of electromechanical systems that advance golf equipment and player performance. This role demands deep practical expertise in mechatronics, sensor systems, electronic hardware, wiring, and embedded controls, combined with the ability to develop supporting software and user-facing tools. The ideal candidate is a builder and problem-solver who thrives in a laboratory and prototype environment, producing functional systems from concept through deployment. Essential Functions and Key Responsibilities: Design, prototype, wire, assemble, and test mechatronic and electromechanical systems used in golf equipment evaluation, performance measurement, and product development Develop and interpret wiring diagrams, schematics, and electrical specifications for custom hardware assemblies, test rigs, and IoT-connected devices Select, integrate, and characterize sensors (IMUs, load cells, encoders, pressure sensors, optical sensors, etc.) – including defining operating limits, calibration procedures, and signal conditioning requirements Design and implement IoT systems and wireless data acquisition platforms that capture real-time performance data from equipment and players Develop embedded firmware and control software for microcontrollers and microprocessors (e.g., Arduino, Raspberry Pi, STM32 or similar) to drive test automation and data capture systems Write clean, maintainable code to interface with hardware, process sensor data, and build internal and user-facing applications and interfaces; leverage AI-assisted development tools to accelerate prototyping and automate test routines; apply basic computer vision techni

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 Synthesia's long-term vision is to build the best human-interactive models: systems that don't just talk to people, but perceive and respond to them, reacting to a user's actions and emotions, not only their words. Today over 60,000 businesses rely on our platform, and the next leap in what we can offer them depends on models that combine text, audio, and video into a single real-time interactive experience. As a Staff Research Engineer, you'll join the Voice team within our 40+ person R&D department, but your scope will extend well beyond voice. You'll help define and drive that broader vision across teams, proposing ambitious research directions and taking direct ownership of the design and implementation of its most critical components. You'll work directly with our voice lead and collaborate tightly with our video teams and other senior members of the org. Concretely, you'll work on voice to voice models that produce text and voice simultaneously. Models that can reason, interrupt the user with back channeling and talk. Models that feel like you are having a natural conversation with, without the feeling of turn taking that is dominant in current speech to speech models. Your role would be

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 a Staff Research Engineer, you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting edge challenges in the Generative AI space, with a focus on avatar-centric interactive video diffusion models. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 60,000 businesses. This is a unique opportunity for experts in machine learning and diffusion models to shape the future of AI video agents that can think, act, and react like humans. As part of our Interactive Avatars Team, you’ll work on cutting-edge research with a clear focus on turning breakthrough ideas into real product capabilities. You’ll join a team that moves fast, iterates often, and builds models that ship and make a meaningful impact. Example tasks and responsibilities include: Adapt diffusion models to incorporate diverse conditioning signals (e.g., audio, motion, interaction cues). Develop methods for streaming infinitely long video sequences at real-time rates. Work on the perceptual layer of interactive agents, including understanding user audio and generating appropriate contextua

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 a Senior Research Engineer, you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting edge challenges in the Generative AI space, with a focus on avatar-centric interactive video diffusion models. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 60,000 businesses. This is a unique opportunity for experts in machine learning and diffusion models to shape the future of AI video agents that can think, act, and react like humans. As part of our Interactive Avatars Team, you’ll work on cutting-edge research with a clear focus on turning breakthrough ideas into real product capabilities. You’ll join a team that moves fast, iterates often, and builds models that ship and make a meaningful impact. Example tasks and responsibilities include: Adapt diffusion models to incorporate diverse conditioning signals (e.g., audio, motion, interaction cues). Develop methods for streaming infinitely long video sequences at real-time rates. Work on the perceptual layer of interactive agents, including understanding user audio and generating appropriate contextu

About the Role As a Research Engineer at Metis, you’ll work on building the next generation of autonomous post-training systems that leverage our Mantis platform. You’ll operate at the intersection of cutting-edge ML research and scalable engineering, designing, implementing, and deploying algorithms that improve how AI agents learn from feedback, synthetic data, and real-world interactions. You’ll move seamlessly between papers and production, leading large-scale experiments, creating optimized training pipelines, and helping shape the future of post-training autonomy. You’ll have significant ownership, high compute budgets, and the mandate to push the state of the art in applied reinforcement and preference optimization. What You'll Do Research and help build an autonomous post-training agent leveraging the Mantis platform Design and execute large-scale experiments on synthetic data generation and algorithmic architecture Develop and refine methods for reinforcement learning, reward modeling, and human feedback integration Collaborate cross-functionally with Core and Platform Engineering to deploy and evaluate models in production settings Publish or contribute to leading-edge research in the post-training domain Use tooling and compute efficiently to iterate on experimental pipelines and accelerate research velocity Requirements Deep experience in machine learning, preferably reinforcement learning, post-training, or alignment research Demonstrated research contributions; ideally published papers (ICML, NeurIPS) or public implementations Strong proficiency in Python and ML frameworks (PyTorch, JAX, or TensorFlow) Comfort with distributed training, high-throughput data pipelines, and large-scale experiment management Ability to reason independently, formulate hypotheses, and run experiments from idea → insight → product impact Compensation & Benefits Base: $200,000–$1,000,000 Significant Equity Full medical, dental, and vision Wellness & L&D stipend Eq

About the Team The Codex Research team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of the Codex Research team, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measu

About the team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval , SWE-bench Verified , MLE-bench , PaperBench , and SWE-Lancer . If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you'll: Create ambitious RL environments to push our models to their limits, and measure frontier

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: Strong excitement about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communication skills and the ability to operate in a cross functional team environment Nice to haves: Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the positi

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: We are looking for Research Engineers to help us redesign how Claude interacts with external data sources. Many of the paradigms for how data and knowledge bases are organized assume human consumers and constraints. This is no longer true in a world of LLMs! Your job will be to design new architectures for how information is organized, and train language models to optimally use those architectures. Responsibilities: Designing and implementing from scratch new information architecture strategies Performing finetuning and reinforcement learning to teach language models how to interact with new information architectures Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data Designing and evaluating advanced agentic search capabilities. You may be a good fit if you: Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using Have good machine learning research experience Have experience developing software that utilizes Large Language Models such as Claude Are results-oriented, with a bias towards flexibility and impact Pick up slack, even if it goes outside your job description Enjoy pair programming (we love to pair!) Want to partner with world-class ML researchers to develop new LLM capabilities Care about the societal impacts of your work Have clear written and verbal communication Strong candidates will also have experience with: Collaborating with product teams to quickly prototype and deliver innovative solutions Building complex agentic systems that utilize LLMs Developing scalable d

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As an Threat Research Engineer at GitLab, you'll research emerging security threats and turn your findings into protection for GitLab customers. You'll investigate new attack surfaces, including AI coding agents, agent plugins, and package ecosystems, and build systems that help detect threats and improve vulnerability advisories. This is a full-stack role: you'll take an idea from the first research question to a working proof of concept, deploy it, and analyze the results yourself. Artificial intelligence (AI) is central to how we work. You'll use AI tools daily and build agentic workflows for research,