Hone builds AI that owns outcomes, not tasks: agentic systems that work towards goals over weeks and months, deploy themselves, and improve over time.
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.
In October 2026, we announced a $60M seed round from Benchmark and Index Ventures.
Half our team are former founders: They’ve built companies, shipped products, and chosen Hone as the place to build next. The rest of us come from Cognition, Mercor, Ramp, Jane Street, Decagon, Brex and Harvey
Our customers are building with AI themselves: Cognition, Modal and Lucanet already use Hone, with customer perspectives featured on our website
Our founders have built from research to scale: They researched agents at Stanford, built the core of Devin at Cognition, led enterprise engineering at Mercor, built internal AI platforms, and founded companies that reached over one million users
We measure our work by economic impact: Engines are deployed across revenue, procurement, risk, product and operations, and we judge them by the results they create, not benchmark scores
Our values shape how we work: Own the Outcome, Set the Standard, Win with Class, and Commit to the Racing Line. Read more on our careers page
Read more: Bloomberg, Index Ventures’ investment thesis, and our launch announcement
Engines are persistent and multiplayer: they run for weeks or months across a company's systems, work towards outcomes autonomously, and pull in people when they need judgment. Most of the interaction patterns for this kind of AI don't exist yet. You'll own the surfaces where people set up, supervise and calibrate engines, end to end from schema to UI: onboarding, real-time activity, review and approvals, routines, memory, permissions, and the tools where people already work.
Engine creation, onboarding and calibration, from first goal to production.
Interfaces that make a long-running agent swarm easy to understand: what it's doing, why, and what it plans to try next.
Task inbox, approvals and review, and the loop that turns each correction into lasting changes in engine behavior.
Frontier interfaces and generative UI: give engines the tools to build custom interfaces that elicit user feedback and judgment in context.
Multiplayer interaction with one persistent engine: shared and personal memory, conflicting feedback, per-person permissions.
Engine surfaces in chat and email.
2+ years of engineering experience, or equivalent depth from open source or your own projects
Time at a fast-growing startup
You've built products from scratch and shipped them to real users
You've owned polished features end to end, from backend to UI
Strong product and design judgment: what to cut, what to polish, when to ship
Strong full-stack fundamentals: Python, TypeScript, React, Postgres, real-time interfaces, debugging unfamiliar code
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 products are made and used
You've founded a company or shipped agent products
You work where design and engineering overlap: you prototype in code and sweat spacing, motion and copy
Intro with a co-founder.
Technical screen.
Product engineering deep dive.
Two-day work trial in San Francisco.
References.
Offer.
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.