Top Scientist Jobs in San Francisco, CA

As a Research Engineer/Scientist, you'll develop AI-empowered cyber tools, run experiments, collaborate on research, and inform policy on advanced AI threats.

The role involves leading medicinal chemistry efforts for peptide therapeutics, overseeing research projects, optimizing drug candidates, collaborating cross-functionally, and mentoring junior scientists.

Lead development, characterization, and qualification of iPSC-derived ocular complex in vitro models (retinal organoids and RPE co-cultures). Design and execute characterization and qualification studies using imaging, transcriptomics, proteomics, and functional assays; analyze complex datasets; maintain SOP-compliant documentation; collaborate cross-functionally and contribute to publications and safety assessments to support ocular drug discovery and translational safety.

Drive applied research and development of deep learning and LLM-based solutions for recommendation systems and real-time bidding. Prototype, train, fine-tune, and deploy production-grade ML models, optimize scalability and performance, and collaborate with engineering, product, and platform teams to deliver large-scale AdTech ML systems.

Perform low-noise, high-frequency electrical measurements on nanoelectrode semiconductor devices in aqueous environments, QC devices, analyze data, create electron-transfer models, perform surface functionalization, interface with engineering for high-throughput testing, and maintain meticulous experimental records.

Design and run experiments to evaluate large AI models across reasoning, style, robustness, and preferences. Develop new metrics and evaluation protocols, analyze large-scale human voting data, collaborate with engineers to productionize findings, prototype research ideas, and publish results to improve model reliability and alignment.

Lead translational biomarker and target discovery efforts for multiple sclerosis using human clinical samples. Design and interpret biomarker assays, support clinical trials, direct lab staff, collaborate cross-functionally and with external partners, and communicate results in protocols, regulatory filings, and publications.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Drive research to advance AI model capabilities for real-world healthcare tasks. Build datasets, environments, and benchmarks; post-train models; publish academic work; and align research with clinical partner needs while working on-site in San Francisco.

Lead immunology-driven translational research across pipeline programs. Design and oversee mechanism-of-action studies (in vitro/ex vivo/in vivo) with CROs, generate and interpret complex immunology datasets, prioritize indications and targets, align preclinical findings with clinical strategy, support external collaborations, author regulatory and scientific documents, and present results internally and externally.

Lead toxicology safety assessment for therapeutic programs (oncology, cardio/respiratory/metabolism) by designing/supervising studies, interpreting data, preparing regulatory documents, communicating with authorities, investigating toxicity mechanisms, and mentoring team members.

Support development, characterization, and scale-up of clinical‑scale CAR‑T manufacturing processes. Execute and analyze DoE, support non‑GMP clinical‑scale operations, contribute to process characterization, tech transfer, documentation, regulatory filings, and cross‑functional project teams to improve CAR‑T manufacturing robustness.

Support development, implementation, and characterization of clinical‑scale CAR‑T manufacturing processes. Design and analyze DoE studies, support non‑GMP and tech transfer activities, contribute to process characterization, continuous improvement, documentation in ELNs and technical reports, and present data to project teams and management.

Support development and characterization of analytical methods (multicolor flow cytometry, cell-based potency/cytotoxicity, molecular assays) for CAR T product release, stability, and GMP manufacturing; perform data analysis, documentation, tech transfer, and cross-functional collaboration for regulatory submissions.

The role involves training and deploying state-of-the-art ML models, with ownership over research projects and familiarity with data engineering.

The Scientist will focus on quantitative bioanalysis, drug metabolite characterization, and method development using LC-MS/MS, contributing to drug metabolism safety and efficacy studies.

Lead design, development, and optimization of electrochemical CMOS sensors and workflows. Develop acid-generation solutions from CMOS electrodes, run advanced electrochemical characterization, analyze data for sensor optimization, communicate results to stakeholders, and mentor junior scientists in cross-functional teams.

Lead development, maintenance, and monitoring of credit risk models for lending portfolios. Use SQL and Python to build pipelines, perform EDA, forecast losses, automate dashboards, produce audit-ready documentation, support model governance/validation, and communicate insights to stakeholders.

The Data Scientist will build predictive models, design data pipelines, conduct analyses, and deploy solutions in a collaborative environment.

Conduct empirical research into AI "scheming": design and run RL experiments on LLMs, build model organisms, develop scalable evaluation techniques, study scaling laws and AI cognition, and collaborate with partner labs to influence deployment and mitigation strategies.

Design, run, and automate pre-deployment evaluations of frontier AI models. Analyze large model transcripts to surface behaviors, build scalable evals, collaborate with AI labs, and improve evaluation pipelines and tooling.

Lead electrolyte innovation for lithium-ion batteries, focusing on high-Ni and silicon-based anodes. Develop formulations, solve interfacial challenges, and ensure commercial viability.

Develop, deploy, and integrate machine learning and deep learning models for scientific and industrial problems (materials, chemistry, process optimization). Build data pipelines, feature engineering, model evaluation, and scalable production integrations. Collaborate with scientists, engineers, product, and customer teams to translate domain problems into AI solutions, support deployments, and communicate results. Conduct strategic research and stay current with AI/ML methods; periodic customer travel.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

The ML Research Scientist will design Bayesian optimization frameworks, collaborate with lab scientists, and integrate ML models with experimental data.