Staff AI Engineer
New
O
OuraAI developer infrastructure
Remote - United StatesFull-TimeStaff
SalaryRegion 1 $198,050 - $233,000; Region 2 $180,200 - $212,000; Region 3 $169,150 - $199,000
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Job Details
- Experience
- 6+ years building and operating production systems that other engineers depend on
- Required Skills
- CI/CDDebuggingDistributed Systems
Requirements
- Bring 6+ years of experience building and operating production systems that other engineers depend on, such as developer tooling, internal platforms, or AI infrastructure at scale.
- Have built and operated agentic coding workflows or autonomous loops in a real engineering organization, with adoption and measured results.
- Understand the software development lifecycle across specification, review, testing, CI/CD, release, and operations.
- Demonstrate technical leadership by setting direction across teams, writing designs, and making architecture and risk decisions.
- Bring strong fundamentals in mobile or backend APIs, concurrency, distributed systems, testing, debugging, and reliability.
- Be fluent in one of backend, iOS, or Android, with the ability to work across platforms and learn new stacks.
- Understand security and privacy considerations when handling sensitive personal data and granting agents access to real systems.
- Be comfortable shaping requirements through experimentation in ambiguous environments.
- Work collaboratively across engineering and non-engineering teams and communicate clearly with stakeholders.
- Nice to have experience with coding agents, evaluation harnesses, sandboxed execution, developer productivity measurement, or mobile app delivery.
Responsibilities
- Set the technical direction and own the architecture of the agentic pipeline, including orchestration, context management, and tool calling.
- Improve pipeline reliability, throughput, cost, and developer experience across mobile, backend, and web repositories.
- Extend agentic workflows across specification, testing, review, CI failure triage and repair, release verification, and post-deploy signals.
- Prototype workflows on real tickets with squads and harden successful approaches into production infrastructure.
- Own evaluation sets, LLM-as-a-judge grading, versioning, tracing, and dashboards for production AI workflows.
- Track and surface merge rate, rework, time-to-signal, cost per change, and escaped defects.
- Design risk-based sandboxing, permissions, human checkpoints, guardrails, and privacy controls.
- Embed with squads to identify pipeline failures and turn successful approaches into reusable paths, skills, templates, and documentation.
- Partner with platform, developer productivity, security, privacy, AI infrastructure, product, design, and engineering teams.
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