Interested in this AI/ML Engineer role at Morningstar?
Apply Now →About This Role
About the Company:
Morningstar’s mission is to empower investor success. The Data and Research team does this by creating products that help financial professionals and the investors they serve, achieve financial goals. The Direct Platform business is one of the largest business units, with a large pre\-existing user group – still prime for market and wallet share growth globally. For the Data and Research business line, this means delivering the data and research that help financial professionals be more efficient, innovative, and connect with their clients in a more meaningful way.
The Role:
We are seeking a strategic and results\-driven Senior Product Marketing Manager to lead the go\-to\-market strategy, positioning, and demand generation for our AI Solutions and Direct Web Services. This role is ideal for a marketer who thrives at the intersection of data, storytelling, and customer insight, and who can translate product innovation into compelling value propositions.
Reporting to the Director of Product Marketing for Data and Research, this individual will partner with Campaign Marketing, Corporate Marketing and the Product, Research, Sales and Client Service teams. This role offers an excellent opportunity to make a real business impact for Morningstar.
Position Location: Chicago, IL (hybrid model with mandatory four days per week in office)
Responsibilities
- Develop positioning, messaging, objection handling, content and sales tools that reflect the attributes and needs of key client segments \& personas. Develop ways to reach each segment with the right value proposition to help qualify and differentiate offers.
- Be ready and willing to dive into data driven initiatives that help define product marketing strategy, prioritization of projects, and development of persona\-based assets. This includes the use of sales dashboards for a deep understanding of the part the business you cover, conducting market sizing, leading win\-loss analysis and more.
- Work with client\-facing teams and subject\-matter experts to ensure client\-feedback, investment insights and responses to sales objections are considered in our client and outcome\-focused marketing materials.
- Craft and execute the go\-to\-market strategy for new product releases, including positioning, content development, and resources for client\-facing teams to deliver business growth, which includes retaining existing clients and attracting new clients.
- Collaborate with 3rd party partners and vendors to plan and implement co\-marketing strategies and activities that promote Morningstar’s solutions to current and new prospects and audiences.
- Develop and execute revenue\-generating sales plays to support in pipeline building and revenue generation. This includes identifying key selling opportunities, creating highly targeted client lists and crafting messaging and resources to enable sellers to capitalize on these opportunities and close deals.
- In alignment with the campaign manager, create compelling persona\-based demand generation campaigns \& campaign resources.
- Partner with corporate and product marketing teams to leverage and extend thought leadership, research, and brand campaigns through our digital, event and media platforms.
- Perform other duties as necessary
Qualifications
- Minimum of 5 years in a product marketing or management capacity – in the financial services industry
- Bachelor’s degree
- Proven ability to communicate effectively, both verbally and in writing, to a wide variety of stakeholders at different levels, translating complex initiatives into materials and products that effectively communicate core values and goals.
- Must have an operational ability to effectively perform at various levels of the business with a proven track\-record of excellence in engaging and building relationships.
- Organized, detail\-oriented and capable of advancing projects simultaneously.
- Must be a strong writer and communicator that brings the voice of the customer front and center to all marketing deliverables.
- Demonstrated ability to develop and position AI‑powered products, with a working understanding of how applied AI and data\-driven insights create customer value. Comfort collaborating with product, data, and research teams to translate AI capabilities into clear, outcome‑oriented messaging and go‑to‑market strategies.
Nice to have
- Comfort with using data to make decisions, strong business acumen, and focus on metrics that drive revenue.
- A team player’s attitude with a high threshold for quality.
- Empathy and the maturity to own and learn from challenges.
- Exceptional prioritization skills within a dynamic environment.
- Self\-starter with a proven commitment to a test, learn, and iterate mindset
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
Total Cash Compensation Range
$95,275\.00\-139,741\.66
Inclusive of annual base salary and target incentive
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.
001\_MstarInc Morningstar Inc. Legal Entity
Salary Context
This $95K-$139K range is in the lower quartile 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($117K) sits 45% below the category median. Disclosed range: $95K to $139K.
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
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