Principal 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 fulfill your career potential! The Opportunity 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. What You’ll Contribute 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 feedforward 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…