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

Lead design and delivery of enterprise-grade platform capabilities: multi-tenancy, identity and authorization, tenant provisioning, metering/billing, audit/compliance, and automation. Act as a hands-on principal engineer across platform, security, and AI/ML teams to build scalable, secure, and operable systems for regulated scientific customers.

Own and prioritize the capability roadmap for Lila's foundational models. Translate stakeholder input into measurable model requirements, define evaluation metrics and release gates, analyze competitor models, and partner with research and training leads to sequence and promote models to production with transparent documentation.

Lead the development and integration of advanced safety systems for scientific AI applications, building a team and establishing a research strategy.

As a Product Lead for Software/Applied AI, you will own specific product areas, guiding full-stack product execution and cross-functional collaboration to build impactful scientific tools.

The AI Lab Research Engineer will develop scientific AI models for complex problems, design and deploy AI agents, and collaborate across disciplines in a lab environment.

As a Senior Software Engineer, you will design APIs, manage database architecture, optimize performance, and collaborate with cross-functional teams to support the Data Platform Team's backend solutions.

Design and implement high-performance UI and APIs, manage database architecture, develop applications, and ensure system scalability and optimization while collaborating with scientists and engineers.

As Commercial Counsel, you will draft and negotiate contracts, provide legal counsel on AI and science, and build scalable legal systems.

The Principal ML Engineer will design and scale ML infrastructure for scientific discovery, collaborating with AI scientists to enhance model training and deployment processes.

Lead the engineering team for Lila's data platform, focusing on architecture, delivery, and team management, while ensuring scalability and performance of data management systems.