Senior Harness Engineer

FICO (NYSE: FICO)is a leading global analytics software company, helping businesses in 100+ countries make better decisions. Join our world-class team today and fulfil your career potential!

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 Senior Harness Engineer you’ll independently own whole harness subsystems, set the standards other engineers build to, and be involved in the end-to-end lifecycle of turning raw model capability into production‑grade engineering.

• Design, build, deploy, and support core 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.
• Own and evolve feed forward guides - agent instruction files, reusable skills, architectural rules, reference docs, and codemods - and drive team‑wide standardisation so 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 automatically before they reach human reviewers.
• Own quality gating and release criteria for agent‑produced work, defining authority boundaries for what agents may merge unaided and the escalation rules for what must route to a human.
• Establish LLM testing infrastructure and evaluation approaches that ensure AI‑generated output meets quality and safety thresholds; apply consumer/contract testing (e.g. Pact) where service integration reliability matters.
• Run the steering loop - when an agent repeats a mistake, engineer a control so it can’t happen again - and treat repository knowledge (docs, specs, context) as the system of record, fighting drift with continuous garbage collection.
• 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.
• 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 - using them to decide where to invest next.
• Partner with product and platform teams to turn specifications and acceptance criteria into enforceable controls.
• Serve as a source of technical expertise and mentor engineers across teams in harness practices and the effective, responsible use of AI tools.

• Bachelor’s/Master’s in Computer Science or related disciplines, or relevant commercial experience in software architecture, design, development, and testing.
• Seasoned software engineer with experience in large, complex codebases and a strong foundation in architecture and design; you care deeply about testing and maintainability.
• 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.
• Proven ability to build engineering tooling across a modern stack -…