Top AI Research Jobs in San Francisco Bay Area, CA

Conduct foundational and applied ML research on AI well-being and moral status, design and run experiments (including tooling/infrastructure), collaborate with external researchers, and publish papers, blog posts, and talks to inform AI developers, policymakers, and the public.

Graduate research interns will own end-to-end projects developing customer world models and proactive intelligence. Work spans representation learning, foundation models, reinforcement learning, agentic decision systems, and evaluation. Tasks include designing methods, running experiments, publishing findings, and potentially shipping models into production to impact customer-facing products.

Graduate research interns will own end-to-end projects to build customer world models and proactive intelligence: developing representation learning, foundation model, RL/agentic methods, evaluation frameworks, and shipping research into production to improve customer decision-making.

Build datasets, evaluations, and scalable data infrastructure to improve frontier AI models for biological and scientific applications. Analyze model failure modes, run experiments, translate scientific expertise into rigorous evaluation criteria, and collaborate with scientists and leading AI labs. The role combines software engineering, biology, and frontier AI in an evolving, highly collaborative environment.

Leads applied AI research and development for foundation models and large-scale machine learning systems. Partners with data scientists, engineers, product managers, and academic researchers to define research directions, train and evaluate models, optimize inference and training, and deploy AI capabilities into products. Mentors applied scientists and managers, represents the organization externally, and translates advanced research into scalable customer experiences.

Own the AI research roadmap from problem identification through production deployment. Develop LLM and deep learning methods for legal text reasoning, extraction, drafting, redlining, hallucination mitigation, explainability, and efficient inference. Build evaluation datasets and benchmarks, assess emerging techniques, collaborate with Engineering and Product, and communicate findings to technical and non-technical audiences.

Conduct research on AI evaluation, rubrics, benchmarks, human judgment, preference data, and agent performance. Design rigorous experiments, validate measurement frameworks, build evaluation pipelines, and create long-horizon real-user benchmarks. Collaborate with engineers, delivery teams, customers, academics, and external labs, while publishing methods and benchmark findings. The role combines applied statistics, messy human data, research prototyping, technical writing, and deployment of research methods in live customer programs.

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Summer 2027 PhD research internship at Atlassian focused on AI research. The role supports distributed-first collaboration, with virtual interviews and flexibility to work remotely or from a company office.

Conducts research evaluating AI-generated digital twins against human responses. Designs experiments, statistical measures, human rating studies, longitudinal data collection, and model evaluations. Analyzes large-scale behavioral data, assesses model calibration and simulation performance, specifies analyses for engineers, and co-authors academic or industry publications.