Head of Engineering
Sondera
Head of Engineering
Sondera
AI agent security and governance platform
AI agents act on what people write, not on everything they mean. Each step can be allowed while the sequence does harm. Sondera's policy engine decides each action from everything the agent has done in the session, using natural-language rules we turn into formal policy. The same action, in the same state, under the same policy, gets the same decision, whichever vendor built the agent. The open problem underneath is semantic integrity: every action carries out what an authorized person meant. We've published our work on it at an ICML workshop and presented 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.
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.
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.
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.
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.
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.
The people who write our papers ship the product, so what breaks in production sets our next question. We study four open problems: turning written rules into policy, enforcing that policy at runtime, making agent behavior inspectable, and overseeing fleets of agents without a person reviewing every action. We build Sondera with coding agents it governs, and a person who owns the code approves every change they make to production. Whoever builds something runs it. We work remotely and in writing, and we flag decisions we can't undo before we make them.
Location: Remote, United States. We meet in person three times a year.
Compensation: Base salary of $230,000–$280,000 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.
Sondera provides security and governance for autonomous AI agents used in business. Its main tool, Sondera Harness, wraps around an organization’s existing agent code and monitors actions at six points in the execution loop, enforcing safety rules written in Cedar from a central place. The platform can be used as a cloud service, a self-hosted system for managing many agents with audit logs, or as a local evaluation option for experiments. Its goal is to help organizations adopt AI agents responsibly by improving reliability, security, and compliance across all autonomous AI applications.
Explore more opportunities at Sondera