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 4 of 4 opportunities
Sr / Principal Product Manager - AI Agent Products
Staff Machine Learning Engineer, Public Sector
Senior Machine Learning Engineer, Public Sector
Senior Design Engineer
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.
- Sep222026
MARL Chapter 9.9: Population-Based Training
Tue, Sep 22, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI [Previous Meeting Recording](https://youtu.be/_BAsCfs4Cr0) This meeting will continue the material from Chapter 9 in [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/). In the last meeting we started the discussion of population based training and the double oracle algorithm in the context of zero-sum games. This time, we will dive deeper into the theoretical limits of the algorithm by comparing it to exact solution techniques with tabular stochastic games. We will start with the zero-sum case and minimax solutions where we can compare the oracle to value iteration. If time permits, we will also consider general sum games where we will need to use a meta solver such as WoLF-PHC to solve the metagame. 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=PLkoCa1tf0XjCU6G
- Sep252026
AAIF Community Denver: Agents as First Class Users
Fri, Sep 25, 2026 · 12:00 AM UTCDenver MLOps CommunityAAIF Community Denver The Denver Agentic AI Foundation is back this month with another great talk. * "MCP-Native by Design: Building Software Where the Agent Is a First-Class User" – Trent Rossiter, Founder & Principal Consultant @ Logic Data Solutions. 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, September 24 🕕 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! ## About AAIF The **Agentic AI Foundation (AAIF)** is a community dedicated to advancing the understanding and practical application of agentic AI. We bring together engineers, researchers, founders, builders, and AI enthusiasts to explore how autonomous AI systems are designed, evaluated, and deployed. Through reading groups, workshops, and community discussions, AAIF brings together engineers, researchers, founders, and AI enthusiasts to exchange ideas, challenge assumptions, and learn from one another. By attending you agree to our [Code of Conduct](https://events.linuxfoundation.org/about/code-of-conduct?utm_source=luma) and [Privacy Policy](https://linuxfoundation.org/legal/privacy-policy?utm_source=luma).
- Sep292026
MARL Chapter 9.9: Population-Based Training
Tue, Sep 29, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI [Previous Meeting Recording](https://youtu.be/_BAsCfs4Cr0) This meeting will continue the material from Chapter 9 in [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/). In the last meeting we started the discussion of population based training and the double oracle algorithm in the context of zero-sum games. This time, we will dive deeper into the theoretical limits of the algorithm by comparing it to exact solution techniques with tabular stochastic games. We will start with the zero-sum case and minimax solutions where we can compare the oracle to value iteration. If time permits, we will also consider general sum games where we will need to use a meta solver such as WoLF-PHC to solve the metagame. 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=PLkoCa1tf0XjCU6G
- Oct62026
MARL Chapter 9.9: Population-Based Training
Tue, Oct 6, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI [Previous Meeting Recording](https://youtu.be/_BAsCfs4Cr0) This meeting will continue the material from Chapter 9 in [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/). In the last meeting we started the discussion of population based training and the double oracle algorithm in the context of zero-sum games. This time, we will dive deeper into the theoretical limits of the algorithm by comparing it to exact solution techniques with tabular stochastic games. We will start with the zero-sum case and minimax solutions where we can compare the oracle to value iteration. If time permits, we will also consider general sum games where we will need to use a meta solver such as WoLF-PHC to solve the metagame. 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=PLkoCa1tf0XjCU6G
- Oct62026
MARL Chapter 9.9: Population-Based Training
Tue, Oct 6, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI [Previous Meeting Recording](https://youtu.be/_BAsCfs4Cr0) This meeting will continue the material from Chapter 9 in [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/). In the last meeting we started the discussion of population based training and the double oracle algorithm in the context of zero-sum games. This time, we will dive deeper into the theoretical limits of the algorithm by comparing it to exact solution techniques with tabular stochastic games. We will start with the zero-sum case and minimax solutions where we can compare the oracle to value iteration. If time permits, we will also consider general sum games where we will need to use a meta solver such as WoLF-PHC to solve the metagame. 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=PLkoCa1tf0XjCU6G
- Oct132026
Reinforcement Learning: Topic TBA
Tue, Oct 13, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI This meeting typically covers topics in reinforcement learning and multi-agent extensions. You can find below links to notes and recordings of 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
AI, Strategy & the Future of Work
Tue, Oct 13, 2026 · 11:30 PM UTCDenver Data DialoguesDenver Data Dialogues In 2026, Datalere will host **Denver Data Dialogues**, an in-person networking event for data practitioners and analytics leaders featuring guest speakers, panels, catered food, and more. A reboot of our former **Data Science in Colorado** meetup, the series will explore **real-world conversations around data strategy, architecture, AI readiness, and analytics leadership**, with practical insights you can take back to work. Events are held on the **second Tuesday of every month** at the Washington Street Community Center. **October: AI, Strategy & the Future of Work** Our October session will feature Rich Steele of Execution Specialists Group (ESG) for a practical conversation about AI and business strategy, why many AI initiatives fail, and what leaders can do differently to set their organizations up for success. Rich will explore the foundations that make AI initiatives successful—from data and business processes to the implications of a hybrid human and digital workforce and the organizational change required for meaningful adoption. The conversation will also touch on AI governance and risk, including what leaders and boards should be thinking about as AI becomes increasingly embedded in the business. The session will be interactive, with plenty of time for discussion and Q&A, giving attendees an opportunity to connect the ideas to the challenges they’re facing in their own organizations. We can’t wait to see you there!
- Oct272026
Reinforcement Learning: Topic TBA
Tue, Oct 27, 2026 · 12:30 AM UTCBoulder Data Science/ML/AIBoulder Data Science, Machine Learning & AI This meeting typically covers topics in reinforcement learning and multi-agent extensions. You can find below links to notes and recordings of 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 This meeting typically covers topics in reinforcement learning and multi-agent extensions. You can find below links to notes and recordings of 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 This meeting typically covers topics in reinforcement learning and multi-agent extensions. You can find below links to notes and recordings of 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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