Staff AI Engineer

ŌURA develops wearable products and a connected app that provide insights into readiness, activity, and sleep. The Staff AI Engineer will architect and operate an agentic software development pipeline across the software lifecycle, making AI-driven workflows reliable, measurable, and safe for engineering teams.

Responsibilities:

• 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:

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• You thrive in ambiguity and shape the requirements yourself through experimentation
• 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
• 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