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About This Role
Senior Manager, AI Partnership Marketing
About the role
Morningstar's Direct Platform is building a dedicated AI Partnership Marketing function to accelerate growth across the LLM and AI ecosystem. As the first marketer dedicated to this space, you'll build and scale a partner marketing program that drives co\-marketing, sales enablement, client education, and measurable pipeline growth.
You'll serve as the primary marketing lead for strategic AI partners, working across Product, Sales, Brand, Legal, and AI Platform Strategy to execute joint go\-to\-market initiatives and grow partner\-sourced revenue.
What you'll own
- Partner marketing strategy and program development
- Co\-marketing campaigns and go\-to\-market execution
- Sales enablement and partner communications
- Partner branding and governance
- Performance measurement and reporting
- Client education and adoption
Responsibilities
- Design, launch, and evolve a scalable AI partner marketing program that supports strategic business priorities.
- Serve as the primary marketing contact for strategic AI partners, driving alignment and execution across cross\-functional teams.
- Develop and execute integrated co\-marketing campaigns, including digital marketing, webinars, events, PR, and co\-branded content.
- Partner with Brand and Product Marketing to establish and maintain consistent brand standards across partner experiences.
- Develop sales enablement resources, including messaging, content, and go\-to\-market materials that support joint sales efforts.
- Measure and communicate partner marketing performance through pipeline reporting, business reviews, and program insights.
- Create client\-facing content that supports awareness, education, and adoption of Morningstar's AI capabilities.
- Partner with Sales leadership and AI Platform Strategy to prioritize partnerships, align go\-to\-market plans, and identify growth opportunities.
- Establish scalable processes and best practices that enable efficient partner collaboration and program execution.
- Manage program budgets, monitor performance, and identify opportunities to improve effectiveness and operational efficiency.
What you bring
- 6\+ years in B2B marketing with at least 2 years in partnership, channel, or alliance marketing at a SaaS or data/technology company.
- Demonstrated experience building or managing a tiered partner program — including co\-marketing execution, enablement, and performance reporting.
- Strong understanding of the LLM and AI platform ecosystem; familiarity with partner marketplace dynamics and co\-sell motions.
- Fluency in demand\-gen channels — paid search, display, social, email, webinars, and events — with the ability to build campaigns across all of them.
- Experience with marketing attribution modeling and CRM hygiene (Salesforce), including pipeline tagging for partner\-sourced and partner\-influenced deals.
- Excellent cross\-functional instincts: comfortable working across sales, legal, brand, product marketing, and executive stakeholders simultaneously.
- Strong project management and written communication skills; able to run multiple partner workstreams without losing fidelity on any of them.
- Experience developing client\-facing education content for non\-technical audiences — translating complex technology into workflow\-level clarity.
- Comfort operating in ambiguity at the early stages of a program build, with the discipline to create repeatable processes from scratch.
Nice to have
- Prior experience at a fintech, financial data, or investment management company — or selling into RIAs, broker\-dealers, or asset managers.
- Familiarity with co\-sell programs at dominant AI platforms (e.g., Microsoft, Salesforce, Google, or similar ecosystem partners).
- Experience working with Gong for sales enablement analytics and asset adoption tracking.
- Background in brand licensing or in\-platform brand governance across third\-party integrations.
How success is measured
Success will be measured by the growth and effectiveness of the partner marketing program, including partner engagement, pipeline and revenue contribution, cross\-functional collaboration, and the successful execution of joint go\-to\-market initiatives.
Chicago, IL (Hybrid, 4/day in office)
Full\-time Reports to: Team Lead, Strategic Account Marketing
Dotted line: Global Head of AI Platform Strategy \& Partnerships
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 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. 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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