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!
TheOpportunity
In this role, you will be a member of a highly talented team of engineers and scientists enabling advanced predictive models deployed into cloud\-based real\-time transaction processing systems. You will be a contributor to FICO products and strategic initiatives that involve analytic innovation with a strong focus on building, testing, and deploying advanced analytics for the FICO Platform. You will have the opportunity to build industry leading solutions and stay at the forefront of modern AI and ML technologies.
What You’ll Contribute
- Contribute to the requirements and development of advanced distributed ML architectures, pipelines, tools, infrastructure, and algorithms that scale up to support both production execution and development of real\-time predictive analytics on large volumes of high\-throughput transactional data.
- Integrate and evaluate advanced ML technologies, platforms and frameworks. Balance performance and cost constraints. Establish strong processes, quality controls, and ethical standards for PII and IP protection, analytic development, and product delivery.
- Interact and collaborate with cross functional teams (Software Development, QA, Professional Services, IT, Product Management, Sales, and Product Support), drive and influence analytic and software designs decisions to ensure successful integration of analytic models into FICO Platform.
- Mentor junior engineers and scientists.
- Travel and work outside of normal business hours as business dictates.
What We’reSeeking
- M.S. or Ph.D. degree in computer science, engineering, physics, or a related technical field
- Industry experience with a proven success record in analytic software, data engineering, or machine learning; experience working with cloud\-based and distributed architectures, large real\-world datasets, and production software or analytics.
- Strong competency in two or more programming languages such as C, C\+\+, Java, or Python; experience working with distributed computing platforms and frameworks, SQL and no\-SQL databases.
- Strong knowledge of computer science fundamentals, software design principles, and best practices such as agile development, unit testing, version control, and continuous integration.
- Experience working in Linux environment and working proficiency with Linux scripting languages.
- Experience building predictive analytic models and data analysis; working knowledge of machine learning fundamentals, mathematics, statistics, and algorithms.
- Strong problem\-solving and communication skills, with the ability to mentor engineers, influence technical direction, and collaborate across disciplines.
Preferred:
- Experience with cloud platforms (AWS, GCP, or Azure) and container orchestration using Docker and Kubernetes.
- Experience with testing frameworks, A/B testing, performance optimization, and production monitoring and observability practices (e.g., Prometheus, Grafana, Datadog, OpenTelemetry).
- Familiarity with AI governance practices: model explainability, prompt versioning, evaluation logging, and audit\-readiness in regulated environments.
- Background in fintech, financial services, insurance, or other regulated domains where model accountability and compliance are paramount.
Our Offerto 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: $122,500 to $192,500 with this range reflecting differences in candidate knowledge, skills and experience.
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 $122K-$192K range is below the median 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $122K to $192K.
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.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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.