Our mission at ŌURA 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

At ŌURA, software engineering velocity directly dictates how quickly we can ship impactful health features, algorithms, and updates to millions of members. We are fundamentally transforming how our engineering teams build and deliver software by embedding agentic automation into our core engineering factory.

As a Staff AI Engineer in Developer Productivity, you will architect and own the systems that make agentic engineering the standard, highly dependable way hundreds of engineers ship code across mobile, backend, and web repositories.

Code generation is only one part of the equation. Your mission is to scale autonomous capabilities across the entire software development lifecycle, including specifications, test creation, automated code review, CI repair, and post-deploy signals. By accelerating feedback loops and removing delivery friction across all teams, your work directly translates into faster iteration cycles and higher-quality releases for ŌURA members.

Key Responsibilities

Set the Technical Direction: Architect, scale, and maintain ŌURA’s agentic pipeline infrastructure, focusing on orchestration, context management, tool calling, and cloud sandboxing.
Close the SDLC Loop: Extend agentic workflows beyond initial code draft generation into spec quality verification, test creation, automated code review, CI failure triage, and release verification.
Rapid Prototyping & Production Hardening: Fast-prototype new agentic workflows against real engineering challenges, then turn validated experiments into robust, production-grade platform tooling.
Measurement & Evaluation: Design and operate evaluation harnesses, LLM-as-a-judge grading systems, tracing, and observability dashboards to measure pipeline health, defect reduction, and time-to-signal.
Autonomy & Guardrails: Establish sandboxing, permissions, and human-in-the-loop checkpoints tailored to risk levels, ensuring privacy-by-design standards across sensitive domains.
Drive Voluntary Adoption: Partner closely with cross-functional engineering squads and AI DRIs to eliminate workflow friction, resolve tooling blockers, and establish reusable paved paths, skills, and templates.

What We Are Looking For

8+ Years of Production Systems Experience: Proven track record of building and operating developer tooling, internal platforms, or production AI infrastructure at scale.
Agentic Workflows in Production: Demonstrated experience designing, deploying, and measuring agentic coding workflows, autonomous execution loops, or background developer automation in real-world engineering environments.
Systems-Level View of the SDLC: Ability to analyze specification, review, build, testing, CI/CD, and deployment as one interconnected system.
Technical Leadership: Experience setting architectural vision, writing clear technical designs, and driving consensus across engineering teams without direct authority.
Engineering Fundamentals: Strong foundational knowledge in distributed systems, concurrency, testing, and debugging, paired with fluency in at least one backend or mobile stack (such as Python, TypeScript, Kotlin, Swift, or Go).
Verification & Security Focus: Disciplined approach to validating AI-generated outputs, combined with a privacy-first mindset when configuring agent privileges and handling sensitive data.

Nice to have

Experience developing custom evaluation harnesses or benchmarks for coding agents.