Member of Technical Staff - Agent Deployments

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

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.