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 3 of 3 opportunities
Product Manager - Integrations
Software Engineer III, AI/ML, Google Cloud
Functional Verification Agentic AI focused AE
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.
- 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)
- Sep12026
Reinforcement Learning: Topic TBA
Tue, Sep 1, 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)
- Sep152026
Reinforcement Learning: Topic TBA
Tue, Sep 15, 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)
- Sep292026
Reinforcement Learning: Topic TBA
Tue, Sep 29, 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)
- Oct132026
Reinforcement Learning: Topic TBA
Tue, Oct 13, 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)
- Oct272026
Reinforcement Learning: Topic TBA
Tue, Oct 27, 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)
- Nov102026
Reinforcement Learning: Topic TBA
Tue, Nov 10, 2026 · 1:30 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)
- Nov242026
Reinforcement Learning: Topic TBA
Tue, Nov 24, 2026 · 1:30 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)
- Dec82026
Reinforcement Learning: Topic TBA
Tue, Dec 8, 2026 · 1:30 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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