Research Chemist Jobs

JLL empowers you to shape a brighter way . Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. Research Analyst – JLL What this job involves: The Research Analyst will join JLL's San Francisco Research team to deliver market intelligence that sets JLL apart. You will analyze San Francisco's industrial, office, and flex markets alongside the economic drivers that shape them. This role combines direct client engagement with hands-on data analysis, positioning you at the intersection of urban economics and commercial real estate. You will grow from foundational market research into automated workflows, quantitative modeling, and independent analysis—building both analytical rigor and technical capability. If you're curious about what drives markets and ready to become data fluent while mastering local real estate dynamics, this role offers a clear path forward. What your day-to-day will look like: Develop expertise on San Francisco's economy, industry composition, demographics, and commercial real estate market to identify trends and emerging opportunities Maintain comprehensive market coverage by tracking significant leasing activity, tenant movements, development projects, sales transactions, and ownership changes Meet regularly with clients and internal teams to present market insights, conditions, and forecasts Produce quarterly reports on San Francisco market

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 Frontier research runs on more than research alone. Behind every model launch, review, and cross-team program is a web of decisions, priorities, and follow-through that keeps hundreds of researchers moving in the same direction. Research Operations owns that work: we bring clarity to ambiguity, provide stability during change, and continually deepen our domain knowledge so that researchers can spend their time on the problems only they can solve. It is one of the highest-leverage functions at Anthropic. We are hiring an experienced operator to embed with the leadership of our reinforcement learning (RL) organization, the team responsible for training Claude to be more capable, more reliable, and more aligned. You will shape what leadership spends its time on, make sure priority decisions reach the right decision-makers and get made quickly, and clear the path so leaders can focus on the work that matters most. You will report to the Manager of Research Operations and work daily with RL org leaders and their teams. Key responsibilities Own leadership's time. Shape the week around the leaders' specific, most timely priorities, set agendas so meetings are spent on decisions that move the org forward, and cut what isn't critical so leaders stay focused on the areas that are highest-leverage for them. Triage what reaches leadership. Resolve what you can, route what belongs elsewhere, and thoughtfully escalate the rest. Enable wise decision-making and follow through. Give leaders what they need to decide quickly, communicate those decisions to collaborators, and track commitments through to completion. See problems coming. Su

About the Team The Frontier Assurance team brings independent scrutiny into OpenAI’s safety decisions and helps the public understand and assess our safety work. We lead third-party assessments and safeguard testing for OpenAI’s flagship launches, pilot new assurance mechanisms such as embedded auditing, run our misalignment disclosure process, and incorporate independent expert input as evidence for critical safety decisions. About the Role As a Research Program Manager on the Frontier Assurance team, you will build programs that bring independent expertise into frontier AI safety decisions and make the evidence behind those decisions understandable to the public. You will lead external research partnerships and third-party assessments, coordinate public safety documentation, and develop new approaches to independent scrutiny and transparency. Working across research, engineering, product, policy, and communications, you will help ensure external findings inform concrete decisions and that our public explanations accurately reflect the evidence, limitations, and remaining uncertainty. We’re looking for people with deep experience in research partnerships and program management with technical and research teams. This role combines partnership management, cross-functional coordination, an understanding of AI safety research, alignment, and evaluations, and strong communication skills. You will work with researchers and engineers within OpenAI and across the external community to initiate projects, set ambitious goals and milestones, and drive execution across multiple teams. 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: Design and run third-party assessment programs for frontier models and safeguards, including independent evaluations, adversarial testing, and new approaches such as embedded auditing. Work with researchers and external part

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.Spotify’s Personalization organization builds the technology that helps millions of listeners discover what they love. Within this space, our research team focuses on advancing the state of the art in machine learning and AI to shape the future of personalization. We explore new approaches, challenge existing assumptions, and contribute to the broader research community while influencing long-term product direction. What You'll Do Conduct original research in machine learning and AI, with a focus on large-scale foundation models, generative AI, and post-training methods, including reinforcement learning. Develop novel methodologies, models, and evaluation frameworks to advance personalization systems. Design and execute rigorous experiments to explore new ideas and validate research hypotheses. Contribute to the scientific community through publications, talks, and conference participation. Collaborate with cross-functional partners to translate research insights into long-term product opportunities. Help define and evolve a forward-looking research agenda aligned with Spotify’s personalization strategy. Mentor others and contribute to a strong, curious, and collaborative research culture. Who You Are You have a Master’s or PhD in machine learning, AI, or a related field, or equivalent research experience. You have demonstrated expertise in machine learning and artificial intelligence through peer-reviewed publications at conferences such as NeurIPS, ICLR, ICML, KDD, or RecSys. <

Position Overview: A Junior Research Analyst within the Diligent Market Intelligence function supports subject matter expertise in specialized research areas such as compensation, governance, activism, voting, and risk. Their main responsibilities include assisting in the design and management of data collection methodologies, overseeing third-party service provider workflows, and ensuring high-quality, timely data delivery. They are also expected to execute manual data collection from global sources, maintain strong quality assurance practices, and collaborate closely with Product and Engineering teams to ensure operational efficiency and accuracy. Additionally, Junior Research Analysts take ownership of specific daily and weekly tasks, contribute to ongoing projects, and play a key role in training service providers while keeping process documentation clear and up to date. The role requires strong numeracy, attention to detail, independent work habits, and a proactive approach to continuously improving research products and processes. Key Responsibilities: Proactive communication and management of outsourcer workload. Conduct data collection tasks to uphold the integrity of our database and keep it current. Execute systematic checks to identify data inaccuracies and operational inefficiencies as per predefined protocols. Provide regular updates and reports to the Vertical lead on progress and challenges. Take ownership of specific daily and weekly tasks, as well as ongoing projects within the assigned research vertically. Required Experience/Skills: Entry level Analytical approach and problem-solving attitude Strong written and verbal communication skills Ability to manage deadlines Ability to adapt to difficult workload demands, e.g., time/resource constraints Proficiency in Microsoft Office, especially Excel About Us Diligent is the AI leader in governance, risk and compliance (GRC) SaaS solutions, helping more than 1 million users and 70

About the Role The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short. As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined. Projects may involve developing new training recipes for self-learning and long-horizon reasoning, curating datasets for non-deterministic scientific and agentic tasks, studying failure modes in agentic behavior, or building infrastructure that enables agent operations at scale. You'll operate in a space where algorithmic innovation, dataset and interaction design, and systems work come together to push the boundaries of what AI agents can reliably accomplish. Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Communicate the plans, progress, and results of projects to the broader team Requirements Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech) Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Understanding of how LLMs work Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Example Research Directions Training, develo

About the Role The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short. As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined. Projects may involve developing new training recipes for self-learning and long-horizon reasoning, curating datasets for non-deterministic scientific and agentic tasks, studying failure modes in agentic behavior, or building infrastructure that enables agent operations at scale. You'll operate in a space where algorithmic innovation, dataset and interaction design, and systems work come together to push the boundaries of what AI agents can reliably accomplish. Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Communicate the plans, progress, and results of projects to the broader team Requirements Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech) Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Understanding of how LLMs work Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Example Research Directions Training, develo

Role Overview As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. This internship is based on-site at our San Francisco HQ, running through the Winter term from January to April. Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details:&n

Role Overview As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details: Our internship program runs 12 to 14 weeks, giving you the opportunity to work alongside industry-leading en

Role Overview As a Research Intern in the Model Shaping team, you will work on one or more of the following areas: Advanced post-training methods across supervised learning, preference optimization, and reinforcement learning New techniques and systems for efficient training of neural networks (e.g., distributed training, algorithmic improvements, optimization methods) Robust and reliable evaluation of foundation model capabilities The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. Past research led by Model Shaping interns resulted in the following publications: Escaping the Verifier: Learning to Reason via Demonstrations (ICML 2026) Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking (ICML 2026) ​​ FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models (ICLR 2026) Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Integrate the research results into Together products Requirements Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Familiarity with the Transformer architecture and recent developments in foundation models Preferred Requirements Prior research experience with training foundation m

Role Overview As a Research Intern in the Model Shaping team, you will work on one or more of the following areas: Advanced post-training methods across supervised learning, preference optimization, and reinforcement learning New techniques and systems for efficient training of neural networks (e.g., distributed training, algorithmic improvements, optimization methods) Robust and reliable evaluation of foundation model capabilities The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. Past research led by Model Shaping interns resulted in the following publications: Escaping the Verifier: Learning to Reason via Demonstrations (ICML 2026) Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking (ICML 2026) ​​ FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models (ICLR 2026) Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Integrate the research results into Together products Requirements Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Familiarity with the Transformer architecture and recent developments in foundation models Preferred Requirements Prior research experience with training foundation m

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. Research Engineer, Synthetic Data About the Role As a Research Engineer on the Synth Data team at SpaceXAI, you’ll invent and scale the synthetic data that teaches our models new capabilities. You own the full loop: spotting a capability the model is missing, designing a data generation strategy, building reliable pipelines and verifiers, and validating that the resulting data actually moves the model. You form hypotheses about what data will help, build the systems to produce it at scale, run ablations, and read the resulting traces and training signals to understand why it worked (or didn’t). Your datasets go directly into frontier training runs, and shape the development of Grok models. What you’ll work on Designing end-to-end synthetic data recipes that turn raw sources (traces, documents, codebases, workflows) into large, diverse, verifiable task sets. Building strong verification and anti-hacking mechanisms so rewards are simple to compute and hard to game. Controlling difficulty and diversity so data stays learnable and reflects real usage instead of collapsing into templates. Running targeted ablations and analyses that demonstrate a dataset caused a capability improveme

Datadog AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin