Head of Engineering

About Sondera

AI agents act on instructions written in natural language, and much of what people mean goes unsaid. A sequence of steps that are each allowed can still do damage. Sondera works on the open problem of semantic integrity: every action an agent takes carries out what an authorized person meant. Enterprise security teams write rules in plain language, and we autoformalize them into policy. Before each action runs, our policy engine renders a verdict given the agent's trajectory: everything the agent has done in the session. The same action, in the same state, under the same policy, gets the same verdict. We publish our research at ICML and NeurIPS workshops and present it at Black Hat Arsenal, BSides and [un]prompted. Our open-source hooks and policy engine already govern coding agents, and enterprise customers run our platform. Our team has built products at Cylance, Databricks, HiddenLayer, NetRise and Obsidian Security, and we're backed by Decibel, Runtime Ventures and Aviso Ventures.

About the role

You lead product engineering. You decide what ships and when, how code is reviewed and whom we hire. You're a player-coach: you review pull requests, own production and still write code. The principal engineers on your team are senior individual contributors who lead through design and review. Our customers' AI agents can't act until Sondera returns a verdict, so our uptime is theirs. Our AI and research teams have their own leads, and their work ships through your team. Your team turns our AI security research into enforceable, systems-level controls. What production teaches us goes back to research.

In your first year you will

• Make deploys, rollbacks and recovery something anyone on the team can run, with SLOs on the services that render verdicts.
• Give every roadmap project an owner, milestones and a date, and ship our endpoint software on dates that hold.
• Ship a research result as a product release every quarter.
• Hold a review bar where a person who owns the code approves every change to production, agent-written or not.
• Give every running component an owner, a cost estimate and a budget.
• Hire and lead principal engineers across platform and AI product surfaces.

You may be a good fit if you

• Have led an engineering team accountable for a production system customers depend on, and still write code.
• Have hired against a written bar and managed performance.
• Find the real defect in a production service or a deploy definition, in whatever systems language you know best.
• Decide on evidence, and write plans and incident reviews people act on.
• Hold a standard through pushback, and give hard feedback early.
• Use coding agents daily, and review what they write as closely as you'd review a colleague's work.

Prior security experience is useful but not required. Judging when research is ready for customers matters more.

Strong candidates may also have

• Production Rust experience.
• A research result you took to production and ran.
• Experience growing an engineering team from a handful of people at an early-stage company.
• Experience with endpoint, authorization or policy-engine software, or software that enterprises run themselves.
• A public record of your work: papers, talks, open source or technical writing.

Our stack

Rust for the services that render verdicts and for embedding in AI surfaces and environments, Python for AI, and TypeScript for the web applications. Policy compiles to a formal policy language. The platform runs in Google Cloud, and on Kubernetes in customers' own infrastructure.

How we work

We study four open problems: turning written rules into policy, enforcing that policy at runtime, making agent behavior inspectable, and overseeing fleets of agents, including agents that delegate to other agents, without a person reviewing every action. The people who write our papers also ship the product, so our research meets agents doing real work, and what breaks sets our next question. Whoever builds something helps run it. We build Sondera with coding agents and govern them with it. We settle technical disagreements with evidence. We work remotely: we start from written intent, review each other's changes async, write decisions down where everyone can read them, and flag any decision we can't undo before we make it.

Logistics

• Location: Remote, United States. We meet in person three times a year.
• Compensation: Base salary of $250,000–$300,000 USD, plus equity.
• Benefits: Medical, dental and vision coverage for you and your dependents; a 401(k) plan; unlimited paid time off, and we expect you to take about a week each quarter, plus paid holidays.
• Visa sponsorship: We sponsor visas, including transfers, though not for every role or candidate.
• Interview process: screen → work-sample interview and review → architecture and operations interview, then a leadership interview → career deep dive (a structured walk through your past roles) → references. We aim to make a decision within two weeks of your screen.
• Using AI in your application and interviews: Use AI to prepare, and to polish an application you wrote yourself. In the work sample you'll build without AI, then extend your work with a coding agent, so we see both how you design on your own and how you check an agent's output. The other interviews are without AI.

We encourage you to apply even if you don't meet every qualification.