Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Provides legal counsel to AI research, product, data, safety, and security teams developing frontier scientific AI systems. Advises on training data, copyright, licensing, privacy, model releases, open-source strategy, research collaborations, autonomous experimentation, biosecurity, AI regulation, export controls, infrastructure, and regulatory inquiries. Builds scalable legal frameworks, playbooks, templates, and self-service guidance for a rapidly evolving AI and life sciences organization.

Lead the inference engineering organization and own Lila’s model-serving platform end to end. Define APIs, serving architecture, batching, routing, deployment, reliability practices, and performance objectives for production LLM systems. Partner with ML, research, product, and executive teams to productionize models, establish evaluation and observability systems, and manage latency, throughput, quality, reliability, and cost. Hire, mentor, and retain engineering talent while setting the serving roadmap and technical strategy.

Own the end-to-end enterprise search and retrieval platform for scientific AI workloads. Build RAG over literature, patents, protocols, and experimental records; develop a scientific knowledge graph; and combine graph, vector, and lexical retrieval into cited, traceable answers. Lead ingestion, document extraction, agentic search, retrieval evaluation, tenancy, permissions, and security. Provide architectural leadership across infrastructure, AI/ML, platform engineering, and scientific teams while coding in Python and deploying cloud-native systems.

Lead full-cycle recruiting for AI/ML research and engineering roles. Develop sourcing strategies, build relationships with researchers and applied scientists, partner with technical leaders on hiring plans, manage interview processes and offers, strengthen employer branding, and maintain recruiting metrics and pipeline rigor. The role requires deep familiarity with AI/ML research talent markets, passive-candidate sourcing, technical evaluation, stakeholder management, and strategic talent planning.

Develop automated safety evaluations, red-teaming methods, reproducible experiments, and monitoring tools for scientific AI models, agents, and tool-mediated workflows. Investigate failure modes, assess evaluator quality, collaborate with domain experts and mitigation engineers, and translate findings into improved safeguards. The role may follow a Research Scientist or Research Engineer path, emphasizing either empirical research and experimental design or reliable research software and evaluation infrastructure.

Lead security investigations for AI systems used in scientific discovery. Develop threat models, test defenses through adversarial assessments and technical analysis, define security requirements, evaluate evidence, and drive improvements with Security, Safety, and engineering teams. The role addresses dangerous misuse, model and data compromise, deployment risks, and safeguards for increasingly capable AI systems.

Build and maintain software, services, data pipelines, and model integrations for AI safety evaluations, red teaming, mitigation experiments, and scientific AI research. Translate researcher needs into reliable tools, preserve reproducible experiment records, and own systems from prototyping through deployment, testing, monitoring, documentation, and improvement. Collaborate with AI and software engineering teams on shared infrastructure.

Develop and implement safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. Translate risk assessments and policy requirements into safeguards spanning training, classifiers, monitoring, access controls, and runtime interventions. Design experiments, evaluate robustness and legitimate-use impacts, investigate failures and distribution shifts, and integrate mitigations with model training, inference, platform, and evaluation teams.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Build provenance infrastructure for scientific data and workflows, including schemas, metadata systems, Python libraries, APIs, and durable records of agent activity. Support reproducible ML, simulation, and drug discovery workflows while transforming research prototypes into scalable production services. Establish engineering practices for testing, packaging, CI, releases, and documentation across local, cloud, and GPU environments. Collaborate closely with scientists and platform teams on storage, compute, and orchestration systems.