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

Build software, interfaces, APIs, and data services that help scientists explore protein designs, plan experiments, and visualize screening results. Collaborate with scientists, engineers, and machine learning researchers, write tested and documented code, incorporate user feedback, and present the completed project. The internship focuses on delivering a practical tool supporting Profluent’s AI-driven protein design workflow.

Own Profluent’s creative function and brand expression across visual identity, video, web, social, events, editorial, and physical assets. Translate complex AI and biology research into clear, engaging work for technical and public audiences. Lead creative projects from concept through publication, manage contractors and vendors, oversee the creative budget, and build a small creative team and scalable design systems over time.

Owns the company’s IT infrastructure, including Google Workspace, identity and access, endpoint security, Meraki networking, cloud platforms, storage, backups, workstations, MDM, software licensing, and user support. Administers Rippling, Slack, Jira, GCP, Oracle Cloud, and Synology systems; maintains security controls, VPNs, MFA, and compliance evidence; manages device provisioning, patching, conferencing equipment, and disaster recovery. Provides hands-on troubleshooting and infrastructure planning in a high-growth startup environment.

Lead the design and development of the NGS analysis platform for gene editing and protein engineering while mentoring team members and collaborating with various teams.

The Machine Learning Scientist will develop reinforcement learning algorithms for protein design, collaborate with teams, and shape machine learning strategies.

Lead and mentor a software engineering team to build and maintain a data platform for protein engineering, utilizing Python and cloud technologies.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.