Interested in this AI/ML Engineer role at Moody's?
Apply Now →About This Role
At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.
If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.
Skills and Competencies* 6+ years of product management experience at a technology, data, or analytics company, including taking new products or major capabilities from early concept to market and driving them to initial commercial traction; required because this role takes a newly launched product through to paying customers
- Hands-on experience building and shipping products powered by artificial intelligence or machine learning, ideally computer vision or vision-language models, including familiarity with model evaluation, quality trade-offs, and the operational realities of non-deterministic systems
- Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI-powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use, with the expectation of developing deep expertise in AI governance and responsible adoption over time in partnership with our machine learning team
- Experience managing products within a large, established portfolio, balancing roadmap investment across many customers, product lines, and stakeholders; required because this role spans a new product and an established property underwriting portfolio
- Demonstrated commercial accountability, including executing product launches, shaping pricing and packaging in partnership with commercial teams, and converting pilot engagements into paying customers; required because this role is measured on adoption and revenue
- Experience leading cross-functional initiatives spanning engineering, design, legal, privacy, risk, and go-to-market functions; required because launches and new capabilities must clear all of these to ship
- Strong customer discovery and enterprise client-facing skills, with the ability to present to senior executives and turn customer feedback into clear product decisions; required for leading carrier pilots and evaluations
- Experience in property and casualty insurance, insurance technology, or property data and analytics is strongly preferred
Education* Bachelor's degree or equivalent practical experience required; an advanced degree in a technical or business discipline is preferred
Responsibilities Lead product management for Moody's AI-powered property underwriting products: strategy, engineering delivery, launch, and growth.* Own the vision, strategy, roadmap, and commercial outcomes for an AI-powered property inspection analytics product as it moves from commercial launch to scaled adoption with insurance carriers
- Extend ownership into the broader property underwriting portfolio, identifying and pursuing opportunities where new data and AI create the most value for insurers
- Collaborate daily with machine learning and platform engineering teams on scope, model quality, evaluation, trade-offs, and delivery, including decisions on when model output is ready to ship and how the product handles model errors
- Partner with the pricing and packaging function to ensure commercial models reflect product value to carriers, and manage the commercial funnel from evaluation agreements through proof of value to paid conversion, with accountability for adoption and revenue targets
- Lead pilot and evaluation relationships with insurance carriers and ecosystem partners, including evaluation plans, results reviews, and turning feedback into roadmap decisions
- Drive the launch and release process for new products and capabilities, including release criteria, sales enablement, training, and support readiness
- Shepherd legal, privacy, risk, and artificial intelligence governance reviews to completion for owned products
About the Team Our CAPE Property Intelligence team is responsible for delivering AI-powered property analytics to the property and casualty insurance industry, serving leading insurers globally. Our team contributes to Moody's by helping insurers assess and price property risk with greater accuracy, by extending Moody's relevance into new, high-value underwriting workflows such as property inspections, and by originating proprietary property data that strengthens Moody's broader analytics and catastrophe modeling portfolio. By joining our team, you will be part of exciting work in applied artificial intelligence for insurance, taking new AI products to market while shaping a property underwriting portfolio that insurers around the world rely on. We embrace AI tools and responsible AI adoption in how we build, operate, and go to market every day.
For US-based roles only: the anticipated hiring base salary range for this position is $124,600.00 - $222,200, depending on factors such as experience, education, level, skills, and location. This range is based on a full-time position. In addition to base salary, this role is eligible for incentive compensation. Moody’s also offers a competitive benefits package, including not but limited to medical, dental, vision, parental leave, paid time off, a 401(k) plan with employee and company contribution opportunities, life, disability, and accident insurance, a discounted employee stock purchase plan, and tuition reimbursement.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, gender, age, religion or creed, national origin, ancestry, citizenship, marital or familial status, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, military or veteran status, or any other characteristic protected by law. Moody’s also provides reasonable accommodation to qualified individuals with disabilities or based on a sincerely held religious belief in accordance with applicable laws. If you need to inquire about a reasonable accommodation, or need assistance with completing the application process, please email [email protected]. This contact information is for accommodation requests only, and cannot be used to inquire about the status of applications
For San Francisco positions, qualified applicants with criminal histories will be considered for employment consistent with the requirements of the San Francisco Fair Chance Ordinance.
This position may be considered a promotional opportunity, pursuant to the Colorado Equal Pay for Equal Work Act.
Click here to view our full EEO policy statement. Click here for more information on your EEO rights under the law. Click here to view our Pay Transparency Nondiscrimination statement.
Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.
Salary Context
This $124K-$222K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Moody's, 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 in Demand for This Role
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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($173K) sits 18% below the category median. Disclosed range: $124K to $222K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Moody's AI Hiring
Moody's has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span US, New York, NY, US. Compensation range: $139K - $236K.
Location Context
AI roles in Austin pay a median of $208,000 across 317 tracked positions. That's 3% below the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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