AI Jobs in Denver & Boulder
The Mountain West's AI hub. Denver and Boulder offer a growing tech scene with great quality of life. Aerospace, fintech, and outdoor tech companies are hiring.
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Latest Denver AI Positions
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AI Engineering Intern
Relocate to SF: Software Engineer (AI Infra)
Senior AI DevOps Engineer
Digital & AI Transformation Director Architecture + Urbanism
Digital & AI Transformation Director Program Management
Remote Chemistry Expert AI Training & Evaluation
Why Denver for AI?
Growing Tech Hub
- Fast-growing startup ecosystem
- Major tech company offices
- Aerospace and defense AI
- Strong fintech presence
- University of Colorado research
Quality of Life
- Lower cost of living than coastal cities
- Outdoor lifestyle and recreation
- Growing tech community
- Boulder startup culture
- Work-life balance focus
Denver AI Compensation
Denver AI salaries are competitive with excellent value. ML engineers earn $140k-$260k+ base, with lower cost of living than coastal hubs.
Upcoming AI Events in Denver
Curated list of AI meetups, hackathons, and conferences coming up in the metro. Auto-refreshed from organizer calendars.
- May292026
MLOps Denver: AI for Quality Engineering and AI with Airflow
Fri, May 29, 2026 · 12:00 AM UTCDenver MLOps CommunityDenver MLOps Community Join us for an evening of **insights and learning** with two engaging talks from industry experts: * "**AI Agents: parallel streams. One Engineer. Many Agents. One Exception Handler.**" – Elliot Sequeria, Senior Manager of Quality Engineering at Luxury Presence * "**Apache Airflow in 2026: New AI provider, Task State Management, and more!**" – Jed Cunningham, Principal Software Engineer at Astronomer This meetup is a great opportunity to **connect with Denver’s AI/ML community**, exchange ideas, and hear from leaders shaping the field. **Event Details:** 📅 **Date:** Thursday, May 28 🕕 **Time:** 6:00 PM – 8:00 PM 📍 **Location:** Code Talent HQ Whether you’re a seasoned data scientist, ML engineer, AI practitioner, or just curious about the latest in ML and AI, come join the conversation!
- Jun22026
Paper Group: Representation Fréchet Loss for Visual Generation
Tue, Jun 2, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI **Join us for a paper discussion on "Representation Fréchet Loss for Visual Generation"** presented by Logan. This paper covers a new differentiable frechet loss (FDrk) that decouples the population size for FD estimation from the batch size for gradient computation. [https://arxiv.org/pdf/2604.28190](https://arxiv.org/pdf/2604.28190) **Silicon Valley Generative AI has two meeting formats:** 1\. Paper Reading \- Every second week we meet to discuss machine learning papers\. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science\. 2\. Talks \- Once a month we meet to have someone present on a topic related to generative AI\. Speakers can range from industry leaders\, researchers\, startup founders\, subject matter experts and those with an interest in a topic and would like to share\. Topics vary from technical to business focused\. They can be on how the latest in generative models work and how they can be used\, applications and adoption of generative AI\, demos of projects and startup pitches or legal and ethical topics\. The talks are meant to be inclusive and for a more general audience compared to the paper readings\. If you would like to be a speaker or suggest a paper email us @ svb.ai.paper.suggestions@gmail.com or join our new [discord](https://discord.gg/xtFVsSZuPG) !!!
- Jun92026
The Future of Work: AI and What Is Yours to Own?
Tue, Jun 9, 2026 · 12:30 AM UTCRocky Mountain AI (RMAIIG)Rocky Mountain AI Interest Group RMAIIG **The Rocky Mountain AI Interest Group (RMAIIG) will host an in-person meeting in Boulder on Monday, June 8th, at 6:30 pm MT on “The Future of Work: AI and What Is Yours to Own?”** 72 Million jobs are going away and 98 million will be created, net new. But who owns the degree of that displacement? Corporations? Ed institutions? Government?... Or is it yours to own? We're bringing three perspectives into conversation. Melissa Reeve, author of Hyperadaptive, will map the workforce transformation already underway: how roles are evolving, what durable skills actually look like, and why she's still bullish on human workers. Susan Adams, Founder of Women in AI Labs, will bring the individual lens: what it means to build an intentional, creative working life with AI and why that distinction matters more than ever in the new world of work. Melissa Whitaker will ground the conversation in a corporate context, having been part of AI transformation motions at Informatica and more recently, Salesforce. **Three lenses. One question: in a world increasingly run with AI, what's yours to own?** You'll leave with a map of where work is going, a framework for designing your place in it, and real-world evidence of what that looks like in practice. **Kudos to Jason Cormier and Susan Adams for organizing and running the meeting!** **Thanks to our pizza sponsor, People Work (people-work.io). It’s easier than ever to build, but it’s harder than ever to get anyone to care. People Work helps serious builders get clear on what they need: technical clarity that ships, authentic messaging that cuts through the noise, and a system that helps build a supportive community. Reach out to annie@people-work.io to learn more.** **The Future of Work: AI and What Is Yours to Own?** **CU Boulder East Campus: In-person** **Aerospace Engineering Sciences Building (Room 120)** **3775 Discovery Dr., Boulder, CO 80303** **Monday, June 8th** **6:30 - 8:30 PM** **5:
- Jun92026
MARL Chapter 9.8.3: Alpha Zero Introduction
Tue, Jun 9, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI This meeting will continue the material from Chapter 9 in [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/). Last time we covered Monte Carlo Tree search and showed how it can be extended to self-play turn taking games. This meeting we will lay the foundation needed to incorporate deep learning into MCTS and how that can be applied to perform policy improvement. We will discuss AlphaZero but also some recent refinements that change the way the policy function is used to direct tree search with Gumbel sampling. As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings. Meetup Links: [Recordings of Previous RL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmy2CNaK-DLailou1VIU1UZn&si=n6uQm863MCcHuKT7) [Recordings of Previous MARL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmzikjw-cNyZfI051ms05czB&si=A7-AeX0dcRW67PDB) [Short RL Tutorials](https://youtube.com/playlist?list=PLYqXmZaxvwmyLEXMpk-n4RFr59tpJjNXt&si=RHy_FAnOJnPa4p1N) [My exercise solutions and chapter notes for Sutton-Barto](https://github.com/jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions) [My MARL repository](https://github.com/jekyllstein/MARL_course/tree/main) [Kickoff Slides which contain other links](https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing) [MARL Kickoff Slides](https://docs.google.com/presentation/d/1FHXGVWkzjKsnNxzVN-29dx5vdkffAx5Vji5nWrDvg1Y/edit?usp=sharing) MARL Links: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) [MARL Summer Course Videos](https://youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2&si=lEljXo65s3fMUsRC) [MARL Slides](https://github.com/marl-book/slides) Sutton and Barto Links: [Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Ba
- Jun92026
Denver Data Dialogues
Tue, Jun 9, 2026 · 11:30 PM UTCDenver Data DialoguesDenver Data Dialogues Come see George Bezerra, founder and CEO of Pristino Labs, speak on *Data Modeling in the Age of AI: Why Your Agents Hallucinate, and How to Fix It.* When agents are fed inconsistent and ambiguous data, they can't be trusted. They hallucinate. They are unpredictable. This isn't an AI problem. It's a data integrity problem. The solution exists. It's been largely forgotten or abandoned in most enterprises, treated as overhead from the on-prem database era. But the discipline that produced trustworthy data then is the same one agents need now. In this talk, you'll learn about the lost art of data modeling. Why it remains the gold standard for data integrity, and how to apply it in your own organization so you can trust your agents to operate at scale.
- Jun232026
Reinforcement Learning: Topic TBA
Tue, Jun 23, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI Typically covers material from the following textbook: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings. Meetup Links: [Recordings of Previous RL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmy2CNaK-DLailou1VIU1UZn&si=n6uQm863MCcHuKT7) [Recordings of Previous MARL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmzikjw-cNyZfI051ms05czB&si=A7-AeX0dcRW67PDB) [Short RL Tutorials](https://youtube.com/playlist?list=PLYqXmZaxvwmyLEXMpk-n4RFr59tpJjNXt&si=RHy_FAnOJnPa4p1N) [My exercise solutions and chapter notes for Sutton-Barto](https://github.com/jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions) [My MARL repository](https://github.com/jekyllstein/MARL_course/tree/main) [Kickoff Slides which contain other links](https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing) [MARL Kickoff Slides](https://docs.google.com/presentation/d/1FHXGVWkzjKsnNxzVN-29dx5vdkffAx5Vji5nWrDvg1Y/edit?usp=sharing) MARL Links: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) [MARL Summer Course Videos](https://youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2&si=lEljXo65s3fMUsRC) [MARL Slides](https://github.com/marl-book/slides) Sutton and Barto Links: [Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto](http://incompleteideas.net/book/the-book.html) [Video lectures from a similar course](https://youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
- Jul72026
Reinforcement Learning: Topic TBA
Tue, Jul 7, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI Typically covers material from the following textbook: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings. Meetup Links: [Recordings of Previous RL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmy2CNaK-DLailou1VIU1UZn&si=n6uQm863MCcHuKT7) [Recordings of Previous MARL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmzikjw-cNyZfI051ms05czB&si=A7-AeX0dcRW67PDB) [Short RL Tutorials](https://youtube.com/playlist?list=PLYqXmZaxvwmyLEXMpk-n4RFr59tpJjNXt&si=RHy_FAnOJnPa4p1N) [My exercise solutions and chapter notes for Sutton-Barto](https://github.com/jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions) [My MARL repository](https://github.com/jekyllstein/MARL_course/tree/main) [Kickoff Slides which contain other links](https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing) [MARL Kickoff Slides](https://docs.google.com/presentation/d/1FHXGVWkzjKsnNxzVN-29dx5vdkffAx5Vji5nWrDvg1Y/edit?usp=sharing) MARL Links: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) [MARL Summer Course Videos](https://youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2&si=lEljXo65s3fMUsRC) [MARL Slides](https://github.com/marl-book/slides) Sutton and Barto Links: [Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto](http://incompleteideas.net/book/the-book.html) [Video lectures from a similar course](https://youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
- Jul212026
Reinforcement Learning: Topic TBA
Tue, Jul 21, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI Typically covers material from the following textbook: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings. Meetup Links: [Recordings of Previous RL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmy2CNaK-DLailou1VIU1UZn&si=n6uQm863MCcHuKT7) [Recordings of Previous MARL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmzikjw-cNyZfI051ms05czB&si=A7-AeX0dcRW67PDB) [Short RL Tutorials](https://youtube.com/playlist?list=PLYqXmZaxvwmyLEXMpk-n4RFr59tpJjNXt&si=RHy_FAnOJnPa4p1N) [My exercise solutions and chapter notes for Sutton-Barto](https://github.com/jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions) [My MARL repository](https://github.com/jekyllstein/MARL_course/tree/main) [Kickoff Slides which contain other links](https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing) [MARL Kickoff Slides](https://docs.google.com/presentation/d/1FHXGVWkzjKsnNxzVN-29dx5vdkffAx5Vji5nWrDvg1Y/edit?usp=sharing) MARL Links: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) [MARL Summer Course Videos](https://youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2&si=lEljXo65s3fMUsRC) [MARL Slides](https://github.com/marl-book/slides) Sutton and Barto Links: [Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto](http://incompleteideas.net/book/the-book.html) [Video lectures from a similar course](https://youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
- Aug42026
Reinforcement Learning: Topic TBA
Tue, Aug 4, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI Typically covers material from the following textbook: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings. Meetup Links: [Recordings of Previous RL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmy2CNaK-DLailou1VIU1UZn&si=n6uQm863MCcHuKT7) [Recordings of Previous MARL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmzikjw-cNyZfI051ms05czB&si=A7-AeX0dcRW67PDB) [Short RL Tutorials](https://youtube.com/playlist?list=PLYqXmZaxvwmyLEXMpk-n4RFr59tpJjNXt&si=RHy_FAnOJnPa4p1N) [My exercise solutions and chapter notes for Sutton-Barto](https://github.com/jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions) [My MARL repository](https://github.com/jekyllstein/MARL_course/tree/main) [Kickoff Slides which contain other links](https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing) [MARL Kickoff Slides](https://docs.google.com/presentation/d/1FHXGVWkzjKsnNxzVN-29dx5vdkffAx5Vji5nWrDvg1Y/edit?usp=sharing) MARL Links: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) [MARL Summer Course Videos](https://youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2&si=lEljXo65s3fMUsRC) [MARL Slides](https://github.com/marl-book/slides) Sutton and Barto Links: [Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto](http://incompleteideas.net/book/the-book.html) [Video lectures from a similar course](https://youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
- Aug182026
Reinforcement Learning: Topic TBA
Tue, Aug 18, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI Typically covers material from the following textbook: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings. Meetup Links: [Recordings of Previous RL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmy2CNaK-DLailou1VIU1UZn&si=n6uQm863MCcHuKT7) [Recordings of Previous MARL Meetings](https://youtube.com/playlist?list=PLYqXmZaxvwmzikjw-cNyZfI051ms05czB&si=A7-AeX0dcRW67PDB) [Short RL Tutorials](https://youtube.com/playlist?list=PLYqXmZaxvwmyLEXMpk-n4RFr59tpJjNXt&si=RHy_FAnOJnPa4p1N) [My exercise solutions and chapter notes for Sutton-Barto](https://github.com/jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions) [My MARL repository](https://github.com/jekyllstein/MARL_course/tree/main) [Kickoff Slides which contain other links](https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing) [MARL Kickoff Slides](https://docs.google.com/presentation/d/1FHXGVWkzjKsnNxzVN-29dx5vdkffAx5Vji5nWrDvg1Y/edit?usp=sharing) MARL Links: [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/) [MARL Summer Course Videos](https://youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2&si=lEljXo65s3fMUsRC) [MARL Slides](https://github.com/marl-book/slides) Sutton and Barto Links: [Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto](http://incompleteideas.net/book/the-book.html) [Video lectures from a similar course](https://youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
Denver & Boulder AI Community & Resources
The Front Range AI scene runs from Denver enterprise practitioners to Boulder research labs, plus Silicon Flatirons policy work and NREL energy AI.
Community Groups & Meetups
- Rocky Mountain AI Interest Group (RMAIIG)
Colorado's flagship AI community with subgroups in Denver, Boulder, and Fort Collins for all experience levels.
- Boulder AI Builders
Community for 3,000+ Colorado AI product builders, alternating Boulder/Denver events every ~6 weeks.
- AI Tinkerers Denver-Boulder
Monthly hands-on meetup for AI engineers with live code demos, part of the global AI Tinkerers network.
- Denver MLOps Community
MLOps and applied AI engineering meetup for practitioners shipping AI systems in production.
Conferences & Festivals
- Silicon Flatirons Annual AI Conference
Full-day Boulder conference on AI policy, infrastructure, and responsible deployment at Colorado Law.
- Boulder Startup Week
Free entrepreneurship festival with dedicated AI/ML tracks, AI Builders Meetup, and pitch competition.
- DenAI Summit
Denver's annual AI-for-public-good summit at the Denver Art Museum — 500+ attendees.
Research Labs
- CU Boulder CAIRO Lab
Bradley Hayes's lab building human-AI teaming techniques for autonomous systems and robotics.
- NREL AI Research (ALIS Group)
Golden, CO lab applying ML, RL, and neurosymbolic AI to energy systems and power grids.
Accelerators & Ecosystem
- Techstars Boulder
The original Boulder accelerator — industry-agnostic with a CU Boulder founder pipeline partnership.
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