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

Develop and apply machine learning methods for protein engineering, including generative design, property prediction, candidate selection, active learning, and sequence or structure modeling. Collaborate with experimental scientists to translate biological objectives into computational problems, evaluate model generalization, interpret wet-lab results, and improve design-test-learn workflows for therapeutic biomolecules.

Develop BioML systems that encode and evaluate mechanistic biological reasoning. Analyze single-cell, perturbation, proteomics, imaging, and genetic data; design experimental campaigns; build outcome-verifiable benchmarks; and connect computational predictions with experimental decisions. The role requires rigorous evidence assessment, uncertainty interpretation, reproducible analysis, and collaboration with ML engineers, experimental scientists, and platform teams.

Build and improve applied AI models for scientific customer workflows. Responsibilities include model post-training, evaluation loop development, experiment design, debugging model behavior, and integrating AI capabilities into production software. The role collaborates with AI researchers and software engineers to adapt models, create reusable tooling, and translate customer feedback into model improvements.

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

Lead an 8–10-person engineering team building LILA’s AI-native application platform, including chat, agents, artifacts, and integrations with laboratory workflows and ML pipelines. Define technical strategy, oversee end-to-end product delivery, ensure reliability and security, collaborate with scientists and cross-functional partners, mentor engineers, and represent the company’s applied-AI work externally.

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.

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