Interested in this AI/ML Engineer role at Morningstar?
Apply Now →Skills & Technologies
About This Role
About Morningstar
Morningstar’s mission is to empower investor success. The Direct Platform team does this by creating products that help financial professionals, and the investors they serve, achieve their financial goals. The Morningstar Direct Platform team is one of the largest business units that strive to give clients access to the data, analytics, and tools that help financial professionals be more efficient, innovative, and connect with their clients in a more meaningful way.
This role is based in our Chicago office, and we follow a hybrid policy of 4 days onsite. Morningstar's hybrid work environment gives you the opportunity to collaborate in\-person each week as we've found that we're at our best when we're purposely together on a regular basis.
Role Overview
We are seeking Forward\-Deployed AI Engineers to rapidly prototype AI solutions in real\-world advisor workflows. This role is embedded in our discovery team , working alongside P roduct M anager s, UX designers, and researchers to translate ideas into functioning prototypes.
Success is measured by speed, feasibility validation, and actionable prototypes that can move downstream.
What Success Looks Like:
- Prototypes valida te advisor wo rkflows quickly and reliably
- Downstream production engineers can operationalize prototypes with minimal friction
Team moves from idea evidence* validated workflow efficiently
- Experiments reduce risk and uncertainty for AI product decisions
Why This Role Matters
Product minded forward\-deployed engineers collapse the gap between concept and reality, enabling the team to make fast, evidence\-based product decisions while reducing wasted effort downstream.
Key Responsibilities
- Build and iterate AI prototypes for advisor workflows
- Integrate signals, models, and UX designs into working experiments
- Test feasibility and performance of AI behaviors in real workflow scenarios
- Collaborate closely with PMs, UX designers, and Research \& Signals Lead
- Capture technical learnings and provide handoff documentation for production teams
- Rapidly val idate id eas before long\-term engineering investment
Required Skills
- AI prototyping – Ability to build working AI systems quickly
- Data integration – Connect signals, APIs, and datasets for prototypes
- Collaboration in cross\-functional teams – Work side\-by\-side with PMs, UX, and researchers
- Experimentation and iteration – Rapidly test and refine ideas with user feedback
- Technical documentation – Clearly communicate prototype behavior and limitations
Qualifications
- 3–5 years of AI engineering experience
- Expert user of AI Coding tools like Claude Code and/or Cursor
- Expertise with Python, LLMs, prompt engineering, and AI framewor ks
- Ability to rapidly prototype with limited infrastructure
- Strong problem\-solving and communication skills
Total Cash Compensation Range
$ 145,170–182,820 USD Annual
Inclusive of annual base salary and target incentive
Compensation and Benefits
At Morningstar we believe people are at their best when they are at their healthiest. That’s why we champion your wellness through a wide range of programs that support all stages of your personal and professional life. Here are some examples of the offerings we provide:
- Financial Health
+ 100% 401k match up to 6% of salary
+ Stock Ownership Potential
+ Company provided life insurance \- 1x salary \+ commission
- Physical Health
+ Comprehensive health benefits (medical/dental/vision) including potential premium discounts and company\-provided HSA contributions (up to $500\-$2,000 annually) for specific plans and coverages
+ Additional medical Wellness Incentives \- up to $300\-$600 annual
+ Company\-provided long\- and short\-term disability insurance
- Emotional Health
+ Trust\-Based Time Off
+ 6\-week Paid Sabbatical Program
+ 6\-Week Paid Family Caregiving Leave
+ Competitive 8\-24 Week Paid Parental Leave
+ Adoption Assistance
+ Leadership Coaching \& Formal Mentorship Opportunities
+ Annual Flex Stipend \- $1000 annually to cover personal education \& well\-being expenses
+ Tuition Reimbursement
- Social Health
+ Charitable Matching Gifts program
+ Dollars for Doers volunteer program
+ Paid volunteering days
+ 15\+ Employee Resource \& Affinity Groups
Morningstar's hybrid work environment gives you the opportunity to collaborate in\-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in\-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.
Salary Context
This $145K-$182K range is below 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 Morningstar, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($163K) sits 24% below the category median. Disclosed range: $145K to $182K.
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.
Morningstar AI Hiring
Morningstar has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $139K - $182K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.