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

Build and scale frontier AI systems for biology: design model architectures, run large-scale pretraining on GPU clusters, develop RL/reward modeling and multi-agent systems, create evaluation frameworks tied to biological outcomes, and publish/open-source research.

Develop and scale frontier AI systems for biology, including foundation models, pre-training pipelines, RL and multi-agent systems, evaluation frameworks, and community-facing publications and open-source releases to accelerate scientific discovery.

Design, build, and optimize custom light-sheet microscopes and imaging subsystems. Develop modular hardware control software (Python, occasional C/C++), integrate cameras, stages, lasers, and sensors, implement automated feedback-driven imaging protocols, and contribute to large-scale data acquisition, visualization, and documentation for reproducible experimental systems.

Design, build, and maintain scalable data infrastructure and tooling for the full data lifecycle to support AI model training and biomedical research. Partner with researchers to enable performant data pipelines, orchestration, storage, observability, and hybrid cloud/HPC compute workflows.

Lead design and operation of hybrid HPC-AI infrastructure supporting large-scale GPU training, low-latency inference, and data-heavy pipelines. Architect and optimize on-prem and cloud GPU resources, integrate MLOps and deployment pipelines, implement advanced scheduling (Slurm, Kubernetes, SUNK), manage storage and networking, support researchers on performance tuning, and drive adoption of AI tooling across teams.

Operate and scale multi-site multi-GPU clusters running Slurm on Kubernetes: ensure reliability and observability, debug storage/network/GPU failures, automate capacity and lifecycle management, drive configuration-as-code, partner with researchers and vendors, and reduce incident MTTR while enabling multi-thousand GPU training runs.

Design and build petabyte-scale data pipelines and platform tooling to ingest, transform, catalog, and deliver heterogeneous biological datasets for AI research. Improve reliability, observability, and automation (agent-based curation/QA) while collaborating with AI researchers and scientists.

Lead AI safety research for biological applications: design safety evaluations, assess emerging capabilities, partner with scientists to build robust safeguards, inform policy, and communicate results to internal and external stakeholders.