Staff Software Engineer, Engineering AI Team

Key Responsibilities

• Build Core Platform Infrastructure: Design, develop, and ship core platform components, including workflow orchestration, third-party integrations, and sandboxed execution environments for AI agents.
• Drive End-to-End Experimentation: Own the full lifecycle of rapid experiments (hypothesis → build → measure → iterate) to discover what meaningfully improves engineering velocity.
• Maintain High-Quality AI Dev Loops: Leverage AI coding agents to write, refactor, and review code, maintaining the strong abstractions and opinionated architecture required for AI to be effective.
• Ground AI in Reality: Act as the technical bridge between experimental AI capabilities and real-world engineering workflows, ensuring our tools solve actual pain points.
• Full-Loop Ownership: Operate autonomously to take concepts from 0 to 1, delivering tangible improvements and clear signals on AI workflow efficacy without over-engineering the solution.

Key Outcomes (first 90 days)

• Master our AI workflow: Integrate our AI tooling while rigorously auditing and refining AI-generated code and tests to meet our quality bar.
• Ship rapidly and reliably: Consistently deliver high-impact PRs with minimal rework by ensuring thorough local testing and CI/CD readiness before review.
• Plan with pragmatism: Adopt our norm of writing lightweight plans (outlining phases, trade-offs, and "good enough" criteria) for non-trivial work before coding.
• Own a full experiment: Drive at least one rapid learning loop completely from hypothesis and implementation to measurement and conclusion.
• Operate with high agency: Drive progress autonomously, raise blockers proactively in real-time, and maintain momentum without step-by-step direction.

Key Outcomes (90+ days)

• Drive continuous experimentation: Run rapid learning loops that clearly connect your daily engineering output to measurable improvements in developer velocity.
• Own complex platform architecture: Autonomously take ambiguous problem spaces from concept to shipped product without over-engineering the solution.
• Set the quality standard: Cultivate an opinionated codebase with solid abstractions and high-value integration tests, actively filtering out low-value AI boilerplate.
• Optimize for speed: Consistently break down complex, multi-layered initiatives into manageable, high-velocity deliverables.
• Act as a force multiplier: Elevate the broader team's velocity through reliable execution, seamless end-to-end ownership, and transparent technical planning.

About You

• Experience Level: Proven track record as a staff engineer with deep experience in software architecture and product development workflows.
• AI-Native Engineering: Genuine enthusiasm for, and experience with, utilizing AI coding agents and integrating AI into daily development and debugging processes.
• Technical Pragmatism: Strong system design instincts combined with a highly pragmatic approach; comfortable building quickly in ambiguous environments.
• SDLC Mastery: Deep, practical experience with modern engineering workflows, including CI/CD pipelines, pull requests, debugging, alerting, and deployment strategies.
• 0 → 1 Mentality: A demonstrated history of rapid prototyping, running learning loops, and taking ambiguous problem spaces to functional, shipped products.