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
Showing 10 of 14 opportunities
Azure AI Data Engineer | Golden, CO, USA
Junior Software Engineer (AI ML)
Senior / Principal Security Architect (AI/OT)
Manager, AI Data Governance Operations
Remote AI Engineer Intern Summer, Part-Time
AI Adoption Leader
Sr AI/ML Engineer
HRIS Analyst, Workday
Staff AI Platform Engineer
Artificial Intelligence (AI) Engineer (LLM / Agent)
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
synced weeklyCurated list of AI meetups, hackathons, and conferences coming up in the metro. Auto-refreshed from organizer calendars.
- Apr212026
Paper Group: LeWorld Model
Tue, Apr 21, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI **Join us for a paper discussion on LeWorld Model** presented by Logan. "LeWorld" by Yann LeCun and team proposes training lejepa representation models from pixels using only a next frame loss and Gaussian regularizer (SigReg). [ https://arxiv.org/pdf/2603.19312]( https://arxiv.org/pdf/2603.19312) **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) !!!
- Apr242026
MLOps Community Denver: AI/ML Happy Hour
Fri, Apr 24, 2026 · 12:00 AM UTCDenver MLOps CommunityDenver MLOps Community 📅 Date: April 23 🕕 Time: 6:00 PM – 8:00 PM 📍 Location: Woods Boss Brewing Company Come join the Denver MLOps Community for a happy hour. No talks, no slides just good vibes, great people, and lots of great AI/ML discussion. See you there! 🍻
- Apr282026
Reinforcement Learning: Topic TBA
Tue, Apr 28, 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)
- May122026
Reinforcement Learning: Topic TBA
Tue, May 12, 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)
- May122026
Denver Data Dialogues
Tue, May 12, 2026 · 11:30 PM UTCDenver Data DialoguesDenver Data Dialogues In 2026, Datalere will host **Denver** **Data Dialogues**, insights for data leaders, an in-person networking event featuring guest speakers, panels, catered food (not pizza) and more! This is a re-boot of our old meetup, **Data Science in Colorado**, but this time with a broader set of topics and new location. Don’t worry, though, the format and quality of speakers hasn’t changed! This event takes place on the **second Tuesday of every month** at the Washington Street Community Center. Arrive early to grab dinner from our catered (not pizza) spread. Stick around to network with your peers. And connect both with people exploring new career opportunities and those actively hiring. By attending Denver Data Dialogues, you will be joining a community of data practitioners and analytics leaders exploring the ideas shaping modern data strategy. These sessions center on **real-world dialogue around data architecture, AI readiness, and analytics leadership**, with an emphasis on collaboration and applicable insight you can take back to work. We can't wait to see you there!
- May262026
Reinforcement Learning: Topic TBA
Tue, May 26, 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)
- Jun92026
Reinforcement Learning: Topic TBA
Tue, Jun 9, 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)
- Jun92026
Denver Data Dialogues
Tue, Jun 9, 2026 · 11:30 PM UTCDenver Data DialoguesDenver Data Dialogues In 2026, Datalere will host **Denver** **Data Dialogues**, insights for data leaders, an in-person networking event featuring guest speakers, panels, catered food (not pizza) and more! This is a re-boot of our old meetup, **Data Science in Colorado**, but this time with a broader set of topics and new location. Don’t worry, though, the format and quality of speakers hasn’t changed! This event takes place on the **second Tuesday of every month** at the Washington Street Community Center. Arrive early to grab dinner from our catered (not pizza) spread. Stick around to network with your peers. And connect both with people exploring new career opportunities and those actively hiring. By attending Denver Data Dialogues, you will be joining a community of data practitioners and analytics leaders exploring the ideas shaping modern data strategy. These sessions center on **real-world dialogue around data architecture, AI readiness, and analytics leadership**, with an emphasis on collaboration and applicable insight you can take back to work. We can't wait to see you there!
- 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)
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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