Member of Technical Staff – Research Engineering

Hone builds AI that owns outcomes, not tasks: agentic systems that work towards goals over weeks and months, deploy themselves, and improve over time. ABOUT HONE Models keep getting smarter, but outside of coding most organizations can't absorb that intelligence, and the gap between model capability and the value derived from it keeps widening. The reasons are many, from human incentives to org design, but at the core we think it's a technology problem. Hone builds engines: AI systems that you hand a goal, like "keep the recruiting pipeline healthy." An engine builds itself, maintains its own agents and code, pulls in people when it needs judgment, and learns from every correction. Leading AI companies and global enterprises already run engines across funnel optimization, procurement, deal sourcing, asset management, financial risk, and product and customer delivery. We measure ourselves by the real-world economic impact our engines create, not benchmark scores. We're ex-founders and early engineers from Cognition, Mercor, Ramp, Jane Street, Decagon, Brex, and Harvey, backed by Benchmark and Index Ventures. THE ROLE Engines run for weeks and months inside real companies, where goals are rarely fully specified and feedback arrives late, partially, and through people. You'll own how engines improve: offline evals, simulations, memory, and the self-improvement loops that turn production feedback into verified changes to skills, memory, code and policies. WHAT YOU'LL WORK ON - Simulations built from company context with simulated users and connectors to evaluate engines on tasks that take weeks or months. - Turning production traces into simulations that we can rewind, fork with one change, and run forward, keeping the simulated world consistent. - Memory systems that store, retrieve, and compact what engines learn. - Self-improvement loops that turn traces into verified changes to skills, memory, code and policies. - Grading and credit assignment over month-long trajectories. REQUIREMENTS - 2+ years of engineering experience, or equivalent depth from research, open source or your own projects - Time at a fast-growing startup - You've shipped applied ML systems from scratch as an engineer, not only papers - You've run evals and experiments rigorously against production systems - Strong fundamentals: ML evaluation, experimental design, statistics, LLM agents - You use coding agents every day and still understand every line you ship - You build and tinker for fun because you genuinely care about how the technology works NICE TO HAVE - Harness, post-training or applied AI experience at a lab or applied AI team - Published research, especially on agent memory or continual learning HOW WE HIRE 1. Intro with a co-founder. 2. Technical screen. 3. Research deep dive. 4. Two-day work trial in San Francisco. 5. References. 6. Offer. LOGISTICS - Location: San Francisco, in person, Sunday to Friday. - Compensation: Competitive salary + meaningful equity. - Visa: We sponsor visas and support you through the immigration process. - Benefits: Health and dental insurance, maternity and parental support, and relocation support.