Interested in this AI/ML Engineer role at FICO?
Apply Now →Skills & Technologies
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 Principal Engineer for Cyber Security GRC \& AI at FICO, you'll serve as the primary architect of how artificial intelligence is integrated into FICO's global security governance, compliance, and risk functions — driving intelligent automation, building risk signal tools and dashboards, and supporting high\-visibility audit engagements across Engineering, Product, Legal, and Security. If you're a cyber security professional who thrives at the intersection of emerging technology and strategic risk management, this role offers something rare: the chance to build something new. You'll be a genuine thought leader — mentoring team members, representing GRC in cross\-functional AI governance forums, and helping customers and regulators understand FICO's approach to responsible AI. You'll work across frameworks at the cutting edge of security and AI regulation, including PCI DSS, SOC 2, ISO 27001, ISO 42001, and the EU AI Act.
WhatYou’llContribute
- Architect and lead the implementation of AI\-powered solutions to automate GRC workflows, including risk assessments, control monitoring, evidence collection, and policy management.
- Serve as the technical voice for AI adoption within the Cyber Security GRC program, defining strategy and roadmap for integrating AI/ML tools across compliance, risk management, and audit functions.
- Lead and coordinate complex, high\-visibility audit engagements, ensuring stakeholder readiness and timely remediation of findings — leveraging AI tools to streamline evidence gathering and reporting.
- Drive the development of intelligent dashboards, risk signal automation, and natural language processing (NLP) tools to improve GRC transparency and decision\-making for leadership.
- Identify opportunities to reduce manual, repetitive GRC processes through automation and AI augmentation, and champion the adoption of these improvements across the team.
- Partner with Engineering, Product, Legal, and Compliance teams to evaluate AI risk, including the governance of AI/ML models used within FICO products, ensuring alignment with applicable regulations and internal policies.
- Respond to and lead the resolution of complex governance, risk, and compliance inquiries from internal and external stakeholders, including customers and regulators.
- Develop and maintain GRC frameworks and standards aligned to PCI DSS, SOC 2, ISO 27001, ISO 42001, NIST CSF, NIST AI RMF, and emerging AI\-specific regulatory requirements (e.g., EU AI Act, NIST AI 600\-1\).
- Mentor and coach junior and mid\-level GRC team members on AI tools, automation techniques, and program best practices to accelerate team capability maturity.
- Act as a subject matter expert and thought leader, representing the GRC team in cross\-functional AI governance discussions, customer calls, and industry forums.
- Assist Corporate Compliance and business units with compliance and security\-related documentation, and provide expert guidance on GRC matters across the organization.
WhatWe’reSeeking
- Extensive experience in Cyber Security, with in\-depth experience focused on GRC.
- Demonstrated experience designing, implementing, or operationalizing AI/ML solutions within a cyber security or GRC context.
- Deep knowledge of GRC industry frameworks and standards, including PCI DSS, SOC 2, ISO 27001, ISO 42001, CSA, NIST CSF, and the NIST AI Risk Management Framework.
- Experience with AI governance frameworks and emerging regulations, including the EU AI Act and NIST AI 600\-1 (Generative AI).
- Experience using or evaluating GRC platforms (e.g., ServiceNow GRC, Archer, OneTrust) and integrating AI/automation capabilities into these tools.
- Proficiency in data analysis, scripting, or programming (e.g., Python) to support automation and AI integration efforts.
- Experience with large language models (LLMs), prompt engineering, or AI\-assisted tools applied to compliance, risk, or audit workflows is highly desirable.
- Ability to translate complex technical and AI concepts into clear business language for executive and non\-technical audiences.
- Proven ability to lead cross\-functional initiatives, manage multiple high\-priority projects concurrently, and deliver results in a fast\-paced, evolving environment.
- Desired certifications: CISSP, CISA, CISM, CRISC; AI\-related certifications (e.g., AWS/Azure/GCP AI certifications, AIGP) are a strong plus.
- Excellent written and verbal communication skills, with demonstrated ability to build trust and productive relationships across business functions and with external partners.
Our Offer to You
- A culture and work environment strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.
- The opportunity to make a difference by leveraging your unique strengths.
- Highly competitive compensation and rewards.
- Flexible work options, opportunities to give back to your community, social events with colleagues and a comprehensive benefits program inclusive of progressive parental leave.
- The targeted base pay range for this role is: $168,000 to $264,000 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 $168K-$264K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($216K) sits 19% above the category median. Disclosed range: $168K to $264K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
FICO AI Hiring
FICO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Diego, CA, US, Remote, US. Compensation range: $192K - $264K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.