Interested in this AI/ML Engineer role at Charles Stewart Mott Foundation?
Apply Now →About This Role
- Date Posted October 8, 2026
- Department Administrative Group
Information Technology
- Location Flint, Michigan, or remote
Summary
The Charles Stewart Mott Foundation invites researchers in AI and computer science to apply for a six-to-12-month fellowship program. Partnering with the Foundation’s chief technology officer and the Information Technology team, the AI fellow will design, build and document AI-enabled workflows, tools and agents alongside program and administrative teams across the Foundation. The fellowship extends the Foundation’s internal AI capability and is intended to produce durable improvements that will remain in place after the fellowship ends. Fellows are given the resources, autonomy and access to real-world operational data needed to pursue ambitious projects — with the goal of producing work that influences how a global $4.4 billion philanthropic foundation operates at scale.
About the AI fellow program
The goal of this fellowship is to allow early-career AI researchers a flexible environment in which to pursue specific research projects while helping to build the Foundation’s AI infrastructure and capabilities.
Working with IT staff and Foundation leaders, the fellow identifies workflows suited to AI-enabled improvement, then designs, builds, tests and documents the AI-assisted tools, workflows and agents needed to support them, using the Foundation’s approved platforms. Clear, maintainable documentation is central to the role, so that workflows can be operated and adapted. Alongside this project work, the fellow provides hands-on training and knowledge transfer to Foundation staff, and tracks and reports on the adoption, usage and impact of what has been built.
Progress is reviewed through regular check-ins with a supervisor, as well as formal reviews at the midpoint of the fellowship and prior to its conclusion, culminating in a final presentation of work and outcomes to Foundation leadership. Throughout, the fellow is expected to surface risks, obstacles or scope changes early and adjust plans in consultation with their mentor and IT leadership.
Fellows propose initial project concepts as part of their application for the fellowship. Project concepts will be refined at the beginning of the fellowship with support from Foundation leaders. Fellows must be able to complete their projects during their fellowship. Projects must align with the Foundation’s goals and priorities.
Each fellow will have access to several valuable opportunities and resources over the course of their fellowship:
- Opportunity to apply for up to $20,000 of compute credits and API credits over the course of the fellowship.
- Opportunity to apply for up to $20,000 in funding for targeted convenings, travel, workshops, working sessions, etc. to advance exploration in various AI project areas.
- Access to more than 100 years of the Foundation’s grantmaking data, representing over $4 billion in cumulative giving.
Duration
Each fellowship will be six months long, with the opportunity to extend up to 12 months, with flexibility around start and end dates to accommodate academic commitments and other needs.
Location
Fellows may be remote or work in the Foundation’s headquarters in Flint, Michigan. Paid travel will be provided to remote fellows to participate in-person consultations and check-ins at the Flint office.
Other Appointments
Fellows may be permitted to keep their existing academic appointments, as long as they do not conflict with either institution’s policies (e.g., conflict of interest policies).
Qualification
The ideal fellow thinks like a researcher but builds like an engineer — someone who can investigate how a new AI capability works, then translate that understanding into a tool or workflow that Foundation staff can use. Graduate students, Ph.D. candidates, recent graduates, postdocs or independent researchers/engineers with demonstrated AI, machine learning or LLM experience are welcome to apply. Candidates should also bring hands-on software development experience and demonstrated programming ability, as this is a build-and-ship role rather than a purely theoretical one. Fellows must operate independently to set and scope their own project agenda and execute it without heavy supervision. They must communicate clearly in writing and in person and be comfortable presenting their work to diverse audiences.
Compensation
$8,000 to $12,000 per month. The fellow’s starting pay will fall within the monthly pay range listed and is determined based on job-related factors including education, work location and experience.
How to apply for this position
Please submit your resume and cover letter expressing your fit for the role and outline of initial project concepts to [email protected].
About the Charles Stewart Mott Foundation
Automotive pioneer Charles Stewart Mott established the private charitable foundation that bears his name in 1926 in Flint, Michigan. The Mott Foundation supports and works with organizations in our hometown of Flint and communities around the world to promote a just, equitable and sustainable society. We make grants to nonprofit organizations working in our four areas of interest: the Flint area, youth engagement, environment and civil society. In 2025, the Foundation made 396 grants totaling more than $161 million. For more information, visit mott.org.
Salary Context
This $96K-$144K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Charles Stewart Mott Foundation, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($120K) sits 43% below the category median. Disclosed range: $96K to $144K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Charles Stewart Mott Foundation AI Hiring
Charles Stewart Mott Foundation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Flint, MI, US. Compensation range: $144K - $144K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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