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

Summer PhD internship building and deploying production ML models for search, recommendations, and matching. Collaborate with engineers and data scientists on experimentation, model evaluation, monitoring, and documentation to improve product discovery and user experience.

Own and deliver full‑stack features end-to-end: design, implement, and ship frontend and backend systems (APIs, async workflows), improve reliability and performance, collaborate with product and design, and mentor peers through code reviews and architecture discussions.

As a Software Engineer, you will build core experiences for students in search and discovery, working with AI technologies and cross-functional teams.

The Senior Software Engineer will drive the development of user platform systems, focusing on user acquisition, onboarding, identity, and fraud prevention while mentoring junior engineers and collaborating across teams.

The Staff Software Engineer will lead development of features for connecting students with employers, architect systems, mentor peers, and drive technical initiatives.

As an ML Research Scientist at Handshake, you will publish papers, develop benchmarks, and collaborate with top AI labs to advance AI evaluation research.

The Chief of Staff to the CEO at Handshake will drive strategic initiatives, ensure alignment across functions, and support high-level decision-making through data analysis and effective communication.

The Senior Engineering Manager of Growth Engineering leads a team to enhance user acquisition, engagement, and retention through scalable platforms and robust technical strategies.

The Strategic Projects Lead will manage and drive AI-related projects, optimize talent deployment, improve internal processes, and enhance customer relationships in a fast-growing environment.

Collaborate with engineers and data scientists to develop, evaluate, and deploy ML models and agentic pipelines for search, recommendations, and discovery. Work on experimentation, performance monitoring, documentation, and production-ready systems while gaining hands-on experience in large-scale ML.