Sr AI Developer Platform/ Platform Engineering

Come join our engineering team in a hands-on technical role at the heart of a new discipline: Harness Engineering. As AI coding agents take on more of the software lifecycle, the hard part is no longer writing code — agents generate it faster than humans can review it, so the bottleneck shifts to verification and trust. Harness Engineering exists to break that bottleneck: engineering the environment that steers agents toward correct, maintainable, well-architected output so that quality is enforced by the system, not re-audited by a person on every change.
We call that environment the harness (Agent = Model + Harness). As a Lead Harness Engineer you'll build and own the individual controls that make up that harness, working end-to-end from problem to production.
This position is not eligible for Visa sponsorship.
What You’ll Contribute
Design, develop, deploy, and support components of the harness — the guides, feedback loops, guardrails, and shared context that turn raw model capability into production-grade engineering. This is a hands-on role focused on systems and leverage, not hand-writing application code.
Build feedforward guides — agent instruction files, reusable skills, architectural rules, reference docs, and codemods — that help agents get it right the first time.
Build feedback sensors — custom linters, static analysis, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers — that catch issues before they reach human reviewers.
Run the steering loop — when an agent repeats a mistake, engineer a control so it can't happen again — and help keep repository knowledge (docs, specs, context) legible to agents, fighting drift.
Contribute to quality gating and release criteria, and to LLM testing that ensures AI-generated output meets quality and safety thresholds.
Help improve observability into agent work and track the measures that matter — cost per merged PR, time-to-merge for agent-assisted PRs, review velocity relative to PR size, defect escape rate, and agent-PR survival rate.
Evaluate the stability, compatibility, scalability, interoperability, and performance of harness components.
Continually learn new techniques in agent-augmented engineering and serve as a source of technical expertise and mentor to junior team members.
What We’re Seeking
Bachelor's/Master's in Computer Science or related disciplines, or relevant experience in software architecture, design, development, and testing.
Strong software engineering background; you've worked in large codebases and care about architecture, testing, and maintainability.
Comfortable building tooling across a modern stack — linters and static analysis, CI pipelines, containerized build/test environments, and instrumentation/observability — and familiar with agent instruction conventions such as AGENTS.md.
Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar) and a feel for where they succeed and fail.
Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work.
A systems mindset — you'd rather fix the environment than fix one output — and the ability to encode "what good looks like" into mechanical, repeatable rules.
Judgement about when to reach for deterministic, computational controls (type checkers, linters, structural/architecture-fitness tests) versus inferential, LLM-based ones (AI code review, LLM-as-judge) — and an understanding of the cost, speed, and reliability trade-offs between them.
Clear communicator; able to articulate design with architects and discuss strategy and requirements with teams.

Work arrangement
Yes