Role description
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.
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
The Role
Every engine runs inside a customer's systems, with a team accountable for its outcome. You'll own getting engines there: scoping customer goals into engine specs, taking engines live with customer teams, owning a set of accounts with an engineering owner, and building the systems that automate deployment until engines deploy themselves.
What You'll Work On
Discovery with customer teams, scoped into engine specs: objectives, evals, permissions and connectors.
Taking engines live, initial testing to autonomous operation.
Operating engines that change their own agents and code after launch: evals, corrections and rollbacks.
Triage of customer asks into configuration, bounded workarounds and product fixes.
Automating deployment itself: build out internal systems that automate use case discovery, scoping, and implementation, and over time turn them into first class product primitives.
Working with the core engineering and product teams to inform upcoming roadmap based on customer feedback and usage.
Requirements
2+ years of experience in engineering, forward-deployed work or a functional domain, or equivalent depth from research, open source or your own projects
Time at a fast-growing startup
A clear spike as an engineer, a forward-deployed engineer, or a functional expert in areas like GTM, finance or operations who builds with AI
You've built something from scratch and stayed with it in production: a deployment, a product or a function
Strong technical fundamentals: APIs, auth, data pipelines, enterprise systems, agents
You turn ambiguous problems into clear specs and know when to cut scope
You use coding agents regularly 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
Deployment experience at an AI startup or lab
Experience designing evals
How We Hire
Intro with a co-founder.
Technical screen.
Deployment case study.
One-day onsite in San Francisco.
References.
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.