Top Scientist Jobs in San Francisco, CA

Lead the technical vision and delivery of production machine learning systems, including distributed training, model serving, lifecycle management, and customer-specific model pipelines. Drive cross-team initiatives, establish engineering best practices, improve ML platform reliability and scalability, mentor senior engineers and data scientists, and connect technical decisions to customer and business outcomes.

Design and execute computational research projects with pharmaceutical clients using clinical, molecular, and multimodal datasets. Analyze oncology and real-world evidence data to support precision medicine, drug discovery, and development. Serve as a trusted technical advisor, co-architect client solutions, communicate complex findings to nontechnical audiences, and collaborate with Research, Engineering, Data Science, Product, and account teams. Contribute to whitepapers, portfolio strategy, workflow improvements, and global account objectives. Travel approximately 25% domestically and internationally.

Drives revenue and partnerships for Tempus data solutions serving pharmaceutical and biotechnology clients. Develops scientifically grounded solutions using real-world evidence, clinical, commercial, and omics data; builds executive relationships; advises customers on research and commercial objectives; communicates technical strategies; manages account opportunities; and collaborates with account, alliance, and operations teams. Requires approximately 30% domestic and international travel.

Own the measurement and improvement of model learning from tasks. Collaborate with frontier labs to design post-training recipes and data-quality techniques, run in-house evaluation systems, develop datasets and data products, assess acquired data, and build scalable ingestion and evaluation systems. The role requires production reinforcement learning experience at a frontier lab and end-to-end LLM post-training expertise.

Lead Vercel’s consumption forecasting strategy and end-to-end machine learning systems across infrastructure products. Develop advanced time-series, probabilistic, hierarchical, causal, and predictive models for operational and financial planning. Build scalable tooling for backtesting, monitoring, drift detection, retraining, and explainability. Partner with Finance, Infrastructure, Product, and GTM leadership on revenue planning, capacity optimization, pricing, and adoption scenarios. Set technical standards, influence organizational ML practices, and mentor senior data scientists and ML engineers.

Lead customer implementations of SOPHiA GENETICS genomic analysis solutions, from planning and sample selection through configuration, training, adoption, and issue resolution. Manage MaxCare Program schedules, timelines, sampling strategies, Statements of Work, technical setup, and cross-functional delivery. Translate laboratory, bioinformatics, data, and clinical regulatory requirements into practical solutions while building trusted customer relationships. The field-based US role includes approximately 30% travel.

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.

This role involves developing AI applications, focusing on LLMs and agent frameworks, and requires research skills in ML with experience in data strategy.

Lead measurement strategy, experimental design, and causal analytics to improve advertiser ROI. Develop measurement roadmaps, partner with Product/Data Science/Sales, prototype scalable solutions, run multi-study meta-analyses, and deliver actionable insights and thought leadership to drive Snap's ads effectiveness.

Drive original research at the intersection of generative modeling, embodied AI, and physical applications, aiming for top journal publications.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Convert ambiguous healthcare and operational problems into well-scoped ML/AI, ranking, optimization, NLP, or LLM tasks; build and evaluate models, run error analysis, design honest evaluations, partner with engineering and clinical stakeholders, and productionize reliable models with clear communication of tradeoffs and limitations.

Lead and conduct ML research aligned to Sprinter Health's strategy: define research agenda, design rigorous experiments, develop novel methods and evaluations, publish and patent findings, translate research into production-ready tools, and collaborate with clinicians and cross-functional teams to validate and deploy clinically robust AI solutions.

Design and deploy optimization, forecasting, and simulation models to match clinicians to patients under constraints. Build baselines, run evaluations and experiments, and productionize decision systems in partnership with engineering, product, and operations to improve operational metrics like cost per visit and clinician utilization.

Lead scientific design and data prototyping for Tempus' multimodal clinical-genomic platform. Perform data evaluation, write technical code (SQL, R, Python), design scalable data models (TDM), validate terabyte-scale datasets across oncology, neurology, and immunology, and translate findings into production-grade data products while advising clients and mentoring cross-functional teams.

Research, design, and deploy multimodal AI models (WSI, genomics, spatial omics, longitudinal records) for precision medicine. Lead algorithmic innovation, collaborate with life-sciences partners on biomarker and CDx solutions, ensure regulatory alignment, and publish peer-reviewed research while delivering production-grade model deployments.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

As a Senior Data Scientist, the role involves leveraging data analysis, experimentation, and statistical methods to influence business decisions and enhance Gusto's product performance, while mentoring team members.

Develop, train, and optimize computer vision and ML models (particularly video-based safety) using petabyte-scale camera and sensor data. Deploy and optimize models for backend and edge inference, collaborate with cross-functional teams, and translate customer needs into scalable ML/CV solutions.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

Design, develop, and productionize multimodal AI systems (VLMs, reasoning models, LLMs) for geospatial data. Build end-to-end ML pipelines, experimentation frameworks, synthetic datasets, and scalable inference on cloud infrastructure while collaborating with research, engineering, and product teams.

Conduct independent robot-learning research from hypothesis through data, model training, deployment, and evaluation. Establish long-term research direction, experiment with novel architectures and post-training methods, develop data strategies, deploy models on physical robots, define real-world metrics, and build scalable machine-learning infrastructure for research and production.

Develops and executes biological, cell-based, biochemical, biophysical, and bioanalytical assays supporting drug discovery and pharmacokinetic research. Responsibilities include ligand-binding assay development, biological sample analysis, in vivo PK study support, experiment design, data interpretation, troubleshooting, high-throughput workflow optimization, laboratory automation, technical reporting, and collaboration with multidisciplinary discovery teams across oncology, inflammation, and virology.

Designs and executes molecular and cellular experiments supporting mRNA, ASO, siRNA, and other therapeutic modalities. Responsibilities include optimizing workflows, developing experimental capabilities, performing qRT-PCR, NGS library preparation, mammalian cell culture, transfection, electroporation, and cell-based screening. The scientist analyzes complex datasets using Benchling, SnapGene, R, and Python, presents findings, evaluates emerging technologies, and collaborates with cross-functional research partners.