Staff AI Engineer

Ōura

7d•$169,150 - $233,000•Hybrid

About The Position

Oura is rebuilding how it builds software, expanding its existing agentic ticket-to-PR pipeline into an end-to-end system used by hundreds of engineers across iOS, Android, backend, and AI services. The Staff AI Engineer on the AI Transformation team will architect and own this system across the software development lifecycle, from specification and intake through planning, implementation, review, testing, CI/CD, release, observability, and incident follow-up. The role combines rapid prototyping with production hardening, and aims to help teams ship validated changes to members faster and more safely. This is a fast-moving, LLM-forward team focused on rapid prototyping, iterative development, and AI-native engineering. The position can be performed remotely from anywhere in Europe or in a hybrid arrangement at Oura's Helsinki, Oulu, or Tampere offices. The engineer will measure pipeline quality and adoption, make workflows dependable and trustworthy, and establish safe, privacy-conscious autonomy proportional to the risk of each task. They will also lead by example, collaborate with engineering teams and cross-functional partners, and help turn successful workflows into reusable paved paths, skills, templates, and documentation.

Requirements

At least 6 years building and operating production systems used by other engineers, including developer tooling, internal platforms, or AI infrastructure at scale; demonstrated outcomes are emphasized over years.
Experience building and operating agentic coding workflows or autonomous loops in a real engineering organization, beyond demos, with adoption and measured results.
Ability to reason across the full software lifecycle, including specification, review, testing, CI/CD, release, and operations.
Technical leadership skills, including setting direction across teams, writing designs others build on, and making architectural and risk decisions.
Strong technical fundamentals in a mobile domain or backend APIs, concurrency, distributed systems, testing, debugging, and reliability; ability to verify AI-generated output.
Fluency in one of backend, iOS, or Android, with willingness and ability to work across other domains and learn new stacks quickly.
Understanding of security and privacy responsibilities involving sensitive personal data and agent access to real systems.
Ability to thrive in ambiguity and shape requirements through experimentation.
Collaborative and resilient approach, including bringing teams along, accepting skeptical feedback, sharing credit, and persevering through failed experiments or changing tools.
Clear communication with engineers, managers, and non-engineering partners.

Nice To Haves

Experience with coding agents in depth, including skills, MCP servers, hooks, sub-agent orchestration, or headless runs.
Experience building evaluation harnesses or benchmarks for coding agents, or LLM-as-judge grading tied to build and test outcomes.
Experience with sandboxed execution at scale, such as ephemeral development environments, VM or container fleets, or macOS build infrastructure.
Background in developer productivity measurement, such as DORA, SPACE, or DX Core 4, and using metrics to inform decisions without turning them into targets.
Experience shipping 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.
Experience mentoring engineers or leading an engineering community of practice.

Responsibilities

Set technical direction for the agentic pipeline and own its architecture, including agent orchestration, context management, and tool calling.
Improve the pipeline's reliability, throughput, cost, and developer experience across mobile, backend, and web repositories; run it unattended where risk permits.
Extend agentic workflows across the software development lifecycle, including specification quality checks, test generation, automated review, CI failure triage and repair, release verification, and post-deploy signals.
Prototype new workflows quickly, validate them on real tickets with real squads, and harden successful approaches into production infrastructure.
Own production AI evaluation and observability, including evaluation sets, LLM-as-a-judge grading, versioning, tracing, and dashboards.
Track and communicate meaningful measures such as merge rate, rework, time-to-signal, cost per change, and escaped defects.
Design sandboxing, permissions, human checkpoints, and guardrails that are proportionate to risk, with privacy by design.
Lead AI-native engineering by example and raise the engineering craft of colleagues.
Work with squads and their AI DRIs to identify and fix pipeline problems on real work, and create reusable paved paths, skills, templates, and documentation.
Partner with platform, developer productivity, security and privacy, AI infrastructure, mobile and backend squads, product and design, and engineering leadership to make trade-offs and align teams.