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About This Role
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
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
Why Make a Move to FICO?
At FICO, you can develop your career with a leading organization in one of the fastest\-growing fields in technology today – Big Data analytics. You’ll play a part in our commitment to help businesses use data to improve every choice they make, using advances in artificial intelligence, machine learning, optimization, and much more.
FICO makes a real difference in the way businesses operate worldwide:
- Credit Scoring — FICO® Scores are used by 90 of the top 100 US lenders.
- Fraud Detection and Security — 4 billion payment cards globally are protected by FICO fraud systems.
- Lending — 3/4 of US mortgages are approved using the FICO Score.
Global trends toward digital transformation have created tremendous demand for FICO’s solutions, placing us among the world’s top 100 software companies by revenue. We help many of the world’s largest banks, insurers, retailers, telecommunications providers and other firms reach a new level of success. Our success is dependent on really talented people – just like you – who thrive on the collaboration and innovation that’s nurtured by a diverse and inclusive environment. We’ll provide the support you need, while ensuring you have the freedom to develop your skills and grow your career. Join FICO and help change the way business thinks!
Learn more about how you can fulfil your potential at www.fico.com/Careers
FICO promotes a culture of inclusion and seeks to attract a diverse set of candidates for each job opportunity. We are an equal employment opportunity employer and we’re proud to offer employment and advancement opportunities to all candidates without regard to race, color, ancestry, religion, sex, national origin, pregnancy, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Research has shown that women and candidates from underrepresented communities may not apply for an opportunity if they don’t meet all stated qualifications. While our qualifications are clearly related to role success, each candidate’s profile is unique and strengths in certain skill and/or experience areas can be equally effective. If you believe you have many, but not necessarily all, of the stated qualifications we encourage you to apply.
Information submitted with your application is subject to the FICO Privacy policy at https://www.fico.com/en/privacy\-policy
Salary Context
This $150K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FICO, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($193K) sits 10% below the category median. Disclosed range: $150K to $236K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FICO AI Hiring
FICO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $165K - $236K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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