The Opportunity

AsaDirector,AI Harness Engineering,you willbuildand leada newdisciplinethat 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 6 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 theharness(Agent = Model + Harness), andwe'rebuilding 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 harnessengineers andsetting the quality bar for AI-assisted engineering across the organization.

What You'll Contribute

• Design, build, and evolve the harness 6 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 andmaintainfeedforward guides(agent instruction files, reusable skills, architectural rules, reference docs, andcodemods) that help agents get it right the firsttime anddrive their adoption across teams.

• Buildfeedback sensors 6 custom linters, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers 6 that catch issues automatically before they reach human reviewers.

• OwnAI governancefor 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 owncross-organizational QA and quality-gating standards, ensuring consistent, enforceable engineering practices across teams and product areas.

• Run thesteering loopat scale 6 when agents repeat a class of mistake, ensure a control is engineered so it cannot happen again 6 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 6 fast checks pre-commit, more expensive checks post-integration, and continuous sensors that scan for drift outside the change lifecycle 6 keeping quality as far left as is economical.

• Establish observability into agent work and own themeasures that matter 6 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 6 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.

• Demonstrateexpertisethrough 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 6 youremainhands-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 6 linters and static analysis, CI/CD pipelines, containerized build/test environments, and instrumentation/observability 6 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 6you'drather fix the environment than fix one output 6 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) 6 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 6 prompt injection, tool/permission scoping, sandboxed execution, and audit trails for agent actions 6 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 Scienceor related discipline, or relevant experience in software architecture, design, development, and testing.