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Staff AI Engineer

ouraRemote - United StatesPosted 22 Oct 2026

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About this role

Our mission at Oura is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their Oura Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles. Empowering the world starts with living our values and empowering our team.

As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office. About the role Oura is rebuilding how it builds software. Every engineer has agentic tooling, and our own pipeline already takes a well-formed ticket to a reviewed, tested commit stack. It runs interactively, headless, and on a cloud sandbox fleet, with its own evals and tracing.

We are looking for engineers who use AI as a fundamental building block of both the products we ship and the way we build them. That is a strong start on one stage of the lifecycle. The next step is the whole loop. As a Staff Engineer on the AI Transformation team, you will architect and own the system that turns agentic development from something power users do into the way hundreds of engineers across iOS, Android, backend and AI services ship every day.

That means spec and intake, planning, implementation, review, testing, CI/CD, release, observability and incident follow-up, each with agents doing real work and humans keeping judgment where it matters. This is a fast-moving, LLM-forward team. We prioritize rapid prototyping, iterative loops and AI-native development, with the speed of a startup and the reach of a product used by millions of members. You can work remotely from anywhere in Europe, or hybrid from our offices in Helsinki, Oulu or Tampere.

You will build the pipeline, measure it, and make it trustworthy enough that teams choose it. Success is measured in validated changes reaching members faster and safer: time to a trustworthy signal, escaped defects, cost per merged change, and how many squads run on the paved path because it is better, not because they were told to. What you will do You don't need to do all of these on day one, but these are the kinds of problems you'll own: Set the technical direction for the agentic pipeline: Make our existing ticket-to-PR pipeline dependable infrastructure.

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Own its architecture, including agent orchestration, context management and tool calling, and improve its reliability, throughput, cost and developer experience across mobile, backend and web repositories. Run it unattended where the risk allows. Close the loop across the SDLC: Extend agentic workflows beyond code generation into spec quality checks, test generation, automated review, CI failure triage and repair, release verification, and post-deploy signals, so the system improves from what happens after merge.

Prototype fast, then harden: Get a new workflow running in days, prove it on real tickets with real squads, and turn what holds up into production infrastructure. Run AI in production properly: Agents in production take more than an API call. Own the eval sets, LLM-as-a-judge grading, versioning, tracing and dashboards that tell us whether a workflow change made things better, including the hardest case: a change that passes CI and is still wrong.

Track the numbers that matter (merge rate, rework, time-to-signal, cost per change, escaped defects) and make them visible to engineers and leadership. Make autonomy safe: Design sandboxing, permissions, human checkpoints and guardrails proportional to risk, with privacy by design as the default. Health data, security and infrastructure changes get stronger controls; low-risk, easily verified work runs hands-off. Lead AI-native engineering by example: Set the bar for AI-augmented engineering across the organization.

Show in your own work what a pipeline-first engineer ships, and raise the craft of the engineers around you. Drive adoption with the teams: Embed with squads and their AI DRIs, find where the pipeline breaks on real work, fix the system rather than the single output, and turn what works into paved paths, skills, templates and docs other engineers can extend. Partner cross-functionally: Work with platform, developer productivity, security and privacy, AI infrastructure, mobile and backend squads, product and design, and engineering leadership to make trade-offs explicit and keep everyone moving on one system instead of five parallel ones.

Requirements We'd love to hear from you if you are: An experienced builder: 6+ years building and operating production systems that other engineers depend on, including developer tooling, internal platforms, or AI infrastructure at scale. We weigh demonstrated outcomes over years. Proven with agentic loops in production: You have built and operated agentic coding workflows or autonomous loops in a real engineering organization, beyond demos: background agents producing PRs, automated review, CI repair, test generation, or similar, with adoption and measured results.

A whole-lifecycle thinker: You reason about specification, review, testing, CI/CD, release and operations as one system, and you know where the bottleneck moves when code generation gets cheap. A technical leader: You set direction across teams you don't manage, write the designs others build on, and are trusted with the hard calls on architecture and risk. Strong on fundamentals: mobile domain or backend APIs, concurrency, distributed systems, testing, debugging and reliability.

You treat AI output as a draft and can show how you verify it. Technically versatile: Fluency in one domain of backend, iOS, or Android, with a stretch into others. You use the tools available to work across platforms and pick up new stacks quickly. Security and privacy conscious: You understand the responsi

Source listing: greenhouse_oura

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