Director - AI Harness Engineering

The Opportunity

As a Director, AI Harness Engineering, you will build and lead a new discipline that lets AI coding agents do reliable work at scale. As 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), and we're building a dedicated Harness Engineering team to own it. This is a hands-on leadership role: you will design and build harness components while leading and growing a regional team of harness engineers and setting the quality bar for AI-assisted engineering across the organization.

What You'll Contribute

• Design, build, and evolve 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; you will contribute code, not just direct it.

• Build and maintain feedforward guides (agent instruction files, reusable skills, architectural rules, reference docs, and codemods) that help agents get it right the first time and drive their adoption across teams.

• Build feedback sensors — custom linters, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers — that catch issues automatically before they reach human reviewers.

• Own AI governance for your region: define authority boundaries for what agents may merge unaided, establish LLM testing infrastructure, and ensure AI-generated output meets quality, safety, and compliance thresholds before release.

• Define and own cross-organizational QA and quality-gating standards, ensuring consistent, enforceable engineering practices across teams and product areas.

• Run the steering loop at scale — when agents repeat a class of mistake, ensure a control is engineered so it cannot happen again — and treat repository knowledge (docs, specs, context) as the system of record, fighting drift continuously.

• Decide where each control runs in the path to production — fast checks pre-commit, more expensive checks post-integration, and continuous sensors that scan for drift outside the change lifecycle — keeping quality as far left as is economical.

• Establish observability into agent work and own 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 — using them to direct where the team invests next.

• Manage, coach, and grow a geographically distributed team of harness engineers; partner with stakeholders to attract talent, set goals, and measure and reward performance.

• Work closely with other engineering leaders and product management to turn specifications and acceptance criteria into enforceable controls, and to align the harness with platform and delivery roadmaps.

• Demonstrate expertise through internal enablement, presentations, and thought leadership on agent-augmented engineering.

What We're Seeking

• Strong software engineering background with experience in large, complex codebases, and genuine care for architecture, testing, and maintainability — you remain hands-on.

• Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar) and a well-developed feel for where they succeed and fail.

• Experience building engineering tooling across a modern stack — linters and static analysis, CI/CD pipelines, containerized build/test environments, and instrumentation/observability — plus familiarity with agent instruction conventions such as AGENTS.md.

• 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.

• Demonstrated experience owning AI governance and cross-organizational quality standards, including establishing LLM testing infrastructure and quality gating for AI-generated artifacts.

• Working knowledge of the security surface unique to autonomous agents — prompt injection, tool/permission scoping, sandboxed execution, and audit trails for agent actions — and how to design least-privilege guardrails around them.

• Strong experience managing geographically distributed, high-performing engineering teams, including navigating the organizational change that AI adoption brings.

• Excellent communication skills to articulate design, strategy, and standards across teams.

• Bachelor's/Master's in Computer Science or related discipline, or relevant experience in software architecture, design, development, and testing.  

Our Offer to You

• An inclusive culture strongly reflecting our core values:  Act Like an Owner, Delight Our Customers and Earn the Respect of Others.

• The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences.

• Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so.

• An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.

• The targeted base pay range for this role is: $150,500 to $236,500 with this range reflecting differences in candidate knowledge, skills and experience.
   

#LI-RR1

#LI-remote