Senior Developer building AI-augmentation pathways for product development at Jane. Creating tools for developer efficiency and aligning efforts within engineering.
Responsibilities
Build the tools and shared infrastructure that make every developer at Jane faster. That spans the agentic context and harness the monolith relies on, the plugins and skills teams reach for daily, the environment that lets agents do real work safely, and the measurement that tells us what's actually landing. Your work multiplies across the whole engineering org, not just one product surface.
Build with a platform mindset, and the judgment to know when it applies. Most of what you make should plug into a larger system we own and maintain, whether that's running agents in the cloud, distributing skills to developers, or managing context. But a quick one-off is sometimes the right way to learn what's real before committing to the durable version. The skill is knowing which is which.
Bring a systems eye to how developers actually work. Some of the time you'll embed alongside product teams, watching how they build and diagnosing where time really goes (CI noise, oversized PRs, review latency, changes-requested churn). The rest of the time you're turning what you saw into tooling that removes the friction rather than papering over it, and that spreads beyond the team you sat with.
Drive agent-authored code from novelty to normal, growing the share of PRs that agents open, land, and merge, and set the bar for how the rest of us build with AI along the way.
Communicate early and often in team channels, sharing context, surfacing risk, and helping everyone stay aligned in a distributed environment. We lean toward over-communicating here, and that's on purpose.
Requirements
Deep, hands-on experience designing systems other developers depend on, with the judgment to know when tooling helps and when it gets in the way.
Hands-on, daily experience building with agentic AI coding tools (Claude Code, Cursor, and similar) across the software development lifecycle. This is the center of the role, not a nice-to-have. We're looking for someone who already delegates real work to agents: writing specs, running multi-file changes, and reviewing output critically.
A track record of building production-grade systems, often from the ground up, paired with real understanding of what it takes to work across large, long-lived codebases with many teams and deep history. That includes the particular problem of getting an LLM the right context to be useful inside all of it.
Genuine curiosity about developer experience and productivity, and the understanding that building a tool is only half the job. The other half is getting it used: the documentation, teaching, and hands-on time with developers that turn a good tool into one people actually reach for. This shows up as a bias to action and an open, collaborative style; the team thrives on people who engage, connect, and share generously.
Experience with evals, prompt and context engineering, or the guardrails that make autonomous agents safe to trust is a real plus. So is a background in observability and quality: both the pre-LLM kind, and the harder question of how you observe and measure systems that don't behave the same way twice. None of it is required, but it's very welcome. Most of what you'll build is in TypeScript, so comfort there matters; Ruby on Rails will help you hit the ground running in the Jane monolith, but it isn't required.
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