Find your next role

Cursor is the AI-native code editor that grew from a few thousand users to one of the highest-revenue developer-tools companies in under two years. Engineering hires are extremely selective — small team, very high product velocity, in-person only. What you'd actually work on: editor performance (it's a fork of VS Code with very heavy AI integration), agent orchestration (Composer, the multi-file edit flow), the diff/merge UX that makes AI edits acceptable to professional engineers, or the inference-serving layer that handles trillions of tokens monthly. Honest fit signals: — You ship fast and don't need a roadmap document handed to you. Cursor engineers are expected to identify their own highest-leverage work and defend the choice to the leadership team weekly. — You're a power user of AI coding tools (ideally Cursor itself) and can articulate where the product is good vs where it falls down. — SF-resident. Cursor is remote-hostile on principle, not as policy theater. Compensation reflects that. What's not a fit: anyone who wants stable hours, anyone who isn't already fluent with React + TypeScript + low-level perf debugging, or anyone looking to learn AI on the job — the bar assumes you already use it daily.

Sierra builds conversational AI agents for enterprise customer experience — think Sonos, ADT, WeightWatchers running their support flows on Sierra's agent platform. Co-founded by Bret Taylor (ex-Salesforce co-CEO, OpenAI board chair) and Clay Bavor (ex-Google Labs). What you'd work on: the agent runtime (deterministic skills + LLM reasoning + tool use), evaluation infrastructure (every agent has to pass thousands of test scenarios before production), customer-specific fine-tuning pipelines, or the developer experience for the people who build agents on the platform. Honest fit signals: — Strong systems engineering background — Sierra agents handle real customer money/data, so reliability and observability matter as much as capability. — You've thought about LLM evals seriously. Sierra's evaluation framework is arguably their biggest moat; engineers who join expecting "just prompt the model" find the real work surprising. — You're motivated by the enterprise-AI problem space (not consumer). The customers are big, demanding, and pay seven figures. What's not a fit: anyone allergic to enterprise sales cycles influencing engineering priorities, anyone who wants pure research, or anyone who needs hard remote.

You'll work directly on training and deploying frontier large language models — pre-training, fine-tuning, RLHF, evals, interpretability, or alignment research, depending on where your strengths fit. What the role actually involves: writing production training code at scale (PyTorch, large distributed clusters), running rigorous experiments with clear hypotheses, and communicating results to research leadership. Less about novel academic ideas; more about disciplined execution against an existing research agenda. Honest fit signals: — You've shipped ML systems that ran in production, not just notebook prototypes. Multi-node training experience matters here. — You're comfortable with the safety-first ethos: this is the team that wrote Constitutional AI and runs the Frontier Red Team. If you're primarily motivated by capability scaling, the culture friction will be real. — Strong written communication. Anthropic's engineering culture is doc-driven; you'll write design docs, postmortems, and research notes more than you'd expect. What's not a fit: pure researchers without engineering taste, engineers without ML systems experience, or anyone who wants a faster product iteration loop than a safety lab will give you.