Staff Software Engineer, Engineering AI Team

About this role

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About The Role

We are looking for a high-agency staff software engineer to help accelerate both platform development and real-world experimentation. This isn’t a typical "build features from a Jira ticket" role. You will operate with high autonomy, owning full loops without handing work off across layers. You will act as a builder, an experiment owner, and a reality check, ensuring that everything we develop is grounded in practical, everyday engineering workflows.

About the Team

We are a small, fast-moving team focused on transforming the software development lifecycle (SDLC) using AI-driven, agentic workflows. Our work sits at the cutting-edge intersection of AI-powered development, rapid experimentation, and robust platform building. We are rethinking how engineers ship code by developing remote coding agents, automated iteration loops, and workflow orchestration. We build and test simultaneously—ensuring that our platform evolves alongside real-world experiments that actually improve engineering velocity.

How We Work

We don't just build AI tools; we walk the walk. This team builds 100% of our software utilizing AI coding agents (e.g., Claude Code) and integrates AI across our entire development and support processes (leveraging Gemini, Claude Cowork, and dogfooding our own platform). We are a team of professionals who recognize a fundamental truth: quality code helps AI agents produce quality code. To that end, we maintain a tight "human-in-the-loop" development cycle, ensuring we cultivate an opinionated codebase with solid abstractions that continuously accelerate our velocity.

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.

We get it. Requirements can sometimes hold people back from applying to a job

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