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

Lead sourcing, evaluating, and commercializing physical-science opportunities (materials, chemistry, energy). Build market and deal models, run primary/secondary research, define specs and commercial theses, and shape partnership/deal structures to launch ventures.

Design and build high-performance UI and APIs, manage database architecture, optimize systems for reliability and performance, and collaborate cross-functionally in a scientific context.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

The Senior Software Engineer will develop systems for scientific workflows, APIs, and applications, collaborating across teams to enhance performance and reliability in production environments.

The Staff Engineer will lead technical direction on data infrastructure, focusing on ingestion frameworks, storage architecture, and orchestration to support scientific workflows. Responsibilities include mentoring, ensuring reliability, and collaborating with cross-functional teams to establish coding standards and data integrity.

Lead the development of software infrastructure for protein design pipelines, ensuring reliable integration of ML systems and experimental workflows.

The Technical Program Manager will lead AI data generation projects, ensuring consistent quality and efficient execution while communicating across teams.

Lead the development and strategy of Lila Sciences' software products, focusing on integrating AI within scientific workflows and managing high-performing teams.

This role invites interested candidates to join a talent community for future job opportunities at Lila.

The Principal Software Engineer will design and build APIs, optimize database architecture, enhance performance, and collaborate on cloud infrastructure for scientific applications.