Top Scientist Jobs in San Francisco, CA

Partner with Growth, Marketing, Sales, and Product to measure and optimize marketing and growth initiatives. Build dashboards and scalable measurement frameworks, design experiments, analyze ROI and trends, provide actionable recommendations, and communicate results to technical and non-technical stakeholders.

The Data Scientist II will lead RWE studies, derive insights from real-world clinical data, and contribute to methodologies using advanced statistical techniques and AI tools, collaborating closely with pharmaceutical partners.

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.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

The Data Scientist II will lead and execute health economic outcomes research projects, collaborate with stakeholders, and build analytical infrastructure while adhering to regulations.

Design, build, and operate large-scale RL post-training systems for multimodal foundation models: distributed training, high-throughput rollout generation, environment and reward infrastructure, evaluation and debugging tooling, and efficiency/stability improvements. Collaborate with researchers to productionize RL methods and run stable RL training across thousands of GPUs.

The Applied AI Scientist will design and implement ML models to decode brain signals, contribute to data platforms, and ensure interpretable results for medical applications.

Develop and deploy tomographic and multi-view 3D reconstruction algorithms for distributed radar and optical sensors, optimize preprocessing, autofocus, and calibration, integrate solutions into cloud production pipelines, and validate performance on real satellite data.

Support R&D to advance phage and yeast display technologies for antibody discovery. Construct and QC display libraries, perform selections, express/purify/characterize molecules (ELISA, flow cytometry, BLI/SPR, CE-SDS), analyze results, collaborate with Data Science/ML on library design and model training, present results, and maintain records in LIMS and electronic notebooks.

Hands-on, bench-driven role to design, optimize, and execute high-throughput antibody developability assays. Drive developability assessments, support late-stage characterization and IND-enabling studies, lead project experimental strategy, automate and integrate methods with LIMS, analyze data, and communicate findings across teams.

Lead development of state-of-the-art generative and predictive ML models for antibody sequence and structure, mentor ML staff, set research strategy, and deploy lab-in-the-loop protein design methods to produce real therapeutic antibodies validated in a high-throughput wet lab.

Lead the simulation initiative at Listen, modeling human preferences through AI. Define research programs and create ground-breaking models for understanding human behavior and decision-making in AI systems.

The Research Engineer/Scientist will advance AI through innovative RL methods, focusing on training general-purpose agents and enhancing existing ML systems.

Research and develop improvements to machine learning models, collaborate with research and product teams, and evaluate model performance.

Conduct research and engineering to improve model capabilities (personalization, instruction following, tool-use). Own research agenda, build robust evaluations, implement and debug code across the ML research stack, and collaborate with product and research teams to deploy models to users.

As a Research Scientist, you will design reinforcement learning environments, collaborating with experts to formalize performance metrics and validate improvements in model capabilities through experiments.

Hands-on role developing and characterizing CHO cell lines, cell culture processes and protein purification. Perform and interpret preclinical molecular assays (flow cytometry, IHC, NGS, microarrays), optimize bioreactor/cell culture processes, develop assays, train/mentor colleagues, and prepare scientific reports and presentations to advance biotherapeutics into clinical studies.

Research and develop quantum-accelerated AI algorithms and theory across generative modeling, probabilistic inference, optimization, and scientific machine learning. Design quantum algorithms and resource estimates, build hybrid quantum-classical workflows, prototype systems in Python/JAX/PyTorch and quantum frameworks, and collaborate with hardware teams to inform architecture and benchmarking.

Drive RL research end-to-end for coding agents: form hypotheses, design experiments, build training/eval/data pipelines, train graders for non-verifiable rewards, improve datapoint quality, and develop realtime RL solutions to scale model performance.

Design and build production-quality medical AI systems, own end-to-end problems, prioritize rigorous evaluation and quantitative proof, collaborate across products, and deliver high-impact, deployed outcomes in a fast-paced, hands-on engineering culture.

As a Research Scientist at Phonic, you'll lead original research in voice AI, drive experimental design, and influence model development directly.

Drive original AI research focusing on new models and algorithms for reasoning in complex environments, collaborating with engineers and contributing to the research community.

Own enzyme characterization and engineering campaigns: design sequence-structure-function-based libraries, perform cloning and library construction, purify and test proteins, develop high-throughput assays and automation, analyze data to inform DBTL cycles, and collaborate with strain and fermentation teams to improve production.

Design and run experiments to evaluate LLM behavior across reasoning, style, robustness, and user preferences. Develop new metrics and evaluation protocols, analyze large-scale human voting/interaction data, prototype research ideas, collaborate with engineers to productionize findings, and publish internal and external research.

Develop and deploy computer-vision and ML pipelines for microscopy datasets, partner with biologists and engineers, benchmark and calibrate tools, extract disease-relevant phenotypes, and communicate results to cross-functional stakeholders.