You push on the hard problems sitting between what models can do today and what customer operations actually need. More science than service: you set the agenda, run the experiments, publish where it makes sense, and hand frontier techniques to the engineers who ship them in six weeks.
What you own. A research agenda across the problems we keep hitting at the edge - agent reliability, long-horizon planning, retrieval over messy operational data, domain-specific evals. Rigorous experiments. Prototypes that cross the gap into production. A tight loop with the Technology Partners and Platform team - what we learn in a construction office is your best dataset.
What you bring. Deep ML background; PhD or equivalent industry research. Track record on applied problems, not just benchmarks - you've shipped research into something a real user touches. Strong engineering fundamentals; you don't throw work over a wall.
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine


