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. 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 responsibility of handling sensitive personal data and of giving agents access to real systems, and you design for both from the start.
• High agency: You thrive in ambiguity and shape the requirements yourself through experimentation.
• Collaborative and resilient: You bring teams along, take feedback from engineers who are skeptical, share credit, and keep going when experiments fail or the tooling changes under you. You communicate clearly with engineers, managers and non-engineering partners.

Nice to Haves

You don't need all of these, but they're nice signals of fit:

• Experience with coding agents at depth: skills, MCP servers, hooks, sub-agent orchestration, headless runs.
• Built evaluation harnesses or benchmarks for coding agents, or LLM-as-judge grading tied to build and test outcomes.
• Experience with sandboxed execution at scale: ephemeral dev environments, VM or container fleets, macOS build infrastructure.
• Background in developer productivity measurement (DORA, SPACE, DX Core 4) and in turning metrics into decisions without turning them into targets.
• Shipped to mobile apps or other environments with slow, expensive feedback loops.
• Experience in digital health, wearables, MedTech, or other privacy-sensitive or regulated products.
• Contributions to open-source AI projects, or a portfolio of projects built on the latest agent capabilities.
• Mentored engineers or led an engineering community of practice.

Benefits

At Ōura, we care about you and your well-being. Everyone here at Ōura has a ring of their own and we are continually looking to improve employee health.

What we offer:

• Competitive salary and equity packages
• Health, dental, vision insurance, and mental health resources
• An Ōura Ring of your own plus employee discounts for friends & family
• 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
• Paid sick leave and parental leave

Ōura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates' pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future.

Region 1 $198,050 - $233,000

Region 2 $180,200 - $212,000

Region 3 $169,150 - $199,000

A recruiter can determine your Region based on your US location.

We are not considering candidates residing in the following states: Alaska (AK), Delaware (DE), Iowa (IA), Mississippi (MS), Nebraska (NE), South Dakota (SD), West Virginia (WV), and Wisconsin (WI).