Top Scientist Jobs in San Francisco, CA

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 research on small language models, develop training methodologies, optimize architectures, ensure AI safety and mentor teams.

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.

Design, research, implement, and deploy scalable deep learning, LLM and generative AI solutions (including RAG and fine-tuning). Advance Responsible AI practices, set research agenda, and collaborate with engineers and scientists to improve model capabilities and reliability.

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.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

As a Staff AI Scientist, you will lead AI projects, design deep learning models using multimodal data, and ensure regulatory compliance in healthcare AI products.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Lead and scale Headway's clinical research: define research roadmap, design and run studies, perform causal inference and experimentation, analyze large real-world datasets, publish peer-reviewed manuscripts, manage budget and resources, and collaborate cross-functionally to inform strategy and demonstrate impact on patient and provider outcomes.

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.

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

Lead analytics and measurement for a Sales subdomain: monitor KPIs, investigate performance issues, design experiments, build data foundations and self-serve dashboards, use agentic AI to automate workflows, and communicate results to senior sales, finance, and operations stakeholders.

Lead analytics for a Sales subdomain: measure performance, investigate drivers, design experiments, and deliver end-to-end data science solutions. Build and maintain data foundations, self-serve dashboards, and communicate results to senior sales, finance, and operations stakeholders. Use agentic AI and modern GTM tooling to scale impact and improve decision-making.

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.

Partner with biopharma clients to design and execute computational translational research using large clinical and molecular datasets. Drive account strategy, co-architect solutions on the Tempus platform, communicate technical results to non-technical stakeholders, author whitepapers, and collaborate with Product and Engineering. Travel ~25%.

Partner with biopharma clients to design and execute computational translational research using large clinical and molecular datasets. Drive account strategy, co‑architect client solutions on the Tempus platform, communicate technical results to nontechnical stakeholders, author thought leadership, and collaborate with Product and Engineering. Travel ~25%.

The Data Scientist will analyze data to provide insights, partner with teams for metrics, develop models, and create accessible visualizations to influence product and program strategies.

Partner with Digital Assets, Finance, Treasury, and Risk to design measurement frameworks, perform strategic analysis and modeling, build scalable SQL-driven automation and dashboards, analyze on-chain and finance data, create visualizations, and drive data-informed product decisions for USDC and related digital asset offerings.

Lead GTM-focused data science initiatives: build revenue forecasts, apply causal inference, develop AI-enabled sales and retention solutions, partner with Product/Growth/Commercial teams, and translate analyses into scalable, automated workflows and executive-ready recommendations.

Lead GTM data science initiatives to build revenue forecasting, causal inference analyses, and AI-enabled solutions that improve sales, retention, and expansion. Partner with Product, Growth, and Commercial teams to operationalize insights, communicate findings to stakeholders, and mentor junior data scientists.

Design, build, and deploy end-to-end machine learning solutions for payment risk and fraud. Responsibilities include data and feature engineering, model training, production deployment, platform/tool development, cross-functional collaboration, and ongoing analysis to mitigate fraud and deliver business impact.