Principal Full Stack Software Engineer

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 own the service behind Sondera's console: the API that people, our assistant and agents all use. You decide how a plain-language draft becomes a policy bound to a group of agents, who may author, review and activate a change, and how customers receive their packages. If change control fails, a customer binds a rule nobody reviewed. The Principal Frontend Engineer on your team owns the web console and its design system; you meet at the API contract. Our AI and research teams have their own leads, and their work reaches customers through the service you own. You'll turn our AI security research into enforceable, systems-level controls.

Principal is a senior individual-contributor role; you lead through design and review.

In your first year you will

• Make one versioned API serve the web console, the assistant and agents over MCP, with one authorization model and one audit record.
• Build change requests with separate rights to author, review and activate, plus staged rollout and rollback.
• Make every change the assistant proposes wait for a person's approval.
• Show a policy change's effect on recorded agent activity before it binds.
• Deliver customer packages from the console without an engineer in the loop.
• Keep the console fast for organizations running tens of thousands of agents.

You may be a good fit if you

• Have designed an API others built on, and evolved it without breaking them.
• Ship typed backend code daily, from database to route. Rust is ideal; strong Go, Java or TypeScript engineers who want to learn it are welcome.
• Have built multi-tenant authorization and audit for people and machines.
• Have shipped LLM features with tool calling and evaluation in production.
• Use coding agents daily, and review what they write as closely as a colleague's work. A person who owns the code approves every change to production.

Prior security experience is useful but not required. Judgment about who may change what matters more.

Strong candidates may also have

• MCP servers or clients, policy engines or approval systems you've built.
• A public record of your work: papers, talks, open source or technical writing.

Our stack

Rust for the API behind the console, TypeScript for the web console, and Python for AI. Agents reach the same API over MCP. 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 $240,000–$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 → API and authorization 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.