Principal Frontend 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 Sondera's web console and its design system. Security engineers, CISOs, analysts and compliance teams use it, in chat and in views, to see, govern and prove what their agents do. You decide how a new organization reaches its first governed agent, how an agent's trajectory and the verdicts on it are explained, and what a reviewer sees before approving a change. You'll turn our AI security research into systems-level controls a security engineer can check.

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

In your first year you will

• Take a new organization to its first governed agent, starting in chat or the web console.
• Hold every merge to journey and accessibility tests.
• Make the design system the contract every view is built from.
• Show what a consequential change will do before anyone approves it: for a policy, what it will allow and deny, with counterexamples and replay against recorded trajectories.
• Decide with users when a conversation serves them and when they need views they can search and filter.
• Keep the web console fast on large fleets of agents and long trajectories.

You may be a good fit if you

• Have shipped an accessible, data-dense web product that stays fast on large datasets, owning its architecture as well as its screens.
• Write production TypeScript and Svelte 5, or React with a fast path to Svelte 5.
• Turn "this is hard to read" into a change you can measure.
• Keep recorded events apart from model-generated labels, and link each to its evidence.
• Use coding agents daily, and review what they write as closely as you'd review a colleague's work. A person who owns the code approves every change to production.

Prior security experience is useful but not required. Caring that a person can verify what the system did matters more.

Strong candidates may also have

• Chat interfaces that render structured results, or generative UI built from a design system.
• Trace, timeline or event-sequence visualizations over large logs.
• Research or product work on scalable oversight of AI agents.
• Agent evaluation or log-analysis tools.
• A public record of your work: papers, talks, open source or technical writing.

Our stack

Rust for the API behind the console, TypeScript and Svelte 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 $210,000–$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 → frontend 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.