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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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3
jobs
Salary distribution
coverage: 33%
<$100k
0
$100–150k
0
$150–200k
1
$200–250k
0
$250k+
0
median$160k
Denver Jobs
3
Growth
Strong
Avg Salary
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Updated
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AI Career Hub • Category snapshot
Market snapshot
Based on the latest 3 jobs shown on this page.
3
jobs in Denver
Salary distribution
coverage: 33%
<$100k
0
$100–150k
0
$150–200k
1
$200–250k
0
$250k+
0
median$160k
Top companies (sample)
AssemblyAI1
XDI Solutions1
Colorado AI News1
Top locations (sample)
Denver, United States1
Denver, Colorado, United States1
Boulder, Colorado, United States1
Want more filters? Open the full job board.

Latest Denver AI Positions

Showing 3 of 3 opportunities

AS

Senior Design Engineer

AssemblyAI
2w ago
Denver, United StatesFull-time
$180k – 240k/yr
View
XD

AI Engineer | US Citizen only (Onsite once a month)

XDI Solutions
3w ago
Denver, Colorado, United StatesFull-time
$140k – 180k/yr
View
CO

Featured AI Job: AI & GenAI Data Scientist-Manager at PwC in Denver

Colorado AI News
3w ago
Boulder, Colorado, United StatesFull-time
$100k – 232k/yr
View
View all 3 denver ai positions

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.

  • Sep
    15
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Sep 15, 2026 · 12:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

  • Sep
    15
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Sep 15, 2026 · 12:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

  • Sep
    15
    2026

    Data Governance and Metadata Architecture for Agentic AI

    Tue, Sep 15, 2026 · 11:30 PM UTCDenver Data Dialogues

    Denver Data Dialogues Every AI initiative eventually hits the same wall. The models are ready. The data is sitting right there. But nothing in the environment knows what the data means, who owns it, how fresh it is, or whether it is safe to put in front of a model. That is not a modeling problem. It is a metadata problem. This month [Datalere](https://datalere.com/) is partnering with [OvalEdge](https://www.ovaledge.com/) for an evening on the least glamorous prerequisite for AI readiness, and the one that decides whether the rest of it works. PLEASE NOTE: this month we are at **CITY PARK TAVERN**, not the Washington Street Community Center, and it is the **THIRD Tuesday** rather than the second. WHO YOU WILL HEAR FROM **[Sharad Varshney](https://www.linkedin.com/in/sharadvarshney/)**, Co-Founder and CEO of OvalEdge, on agentic data governance: what AI agents can genuinely take off your plate in discovery, classification, lineage, and policy monitoring, and where people stay in the loop to approve and guide. **[Sean Hewitt](https://www.linkedin.com/in/sean-hewitt-9577585/)** on metadata architecture. Sean authored Eckerson Group's white paper on metadata governance and works with Datalere on client engagements. Six metadata domains, four maturity levels, four steps to get from chaos to a working standard, tuned for a world where agents both consume metadata and generate it. A panel moderated by **[Carlos Bossy](https://www.linkedin.com/in/carlosbossy/)**, CEO and Chief Data Architect at Datalere, with Sharad, Sean, **[Shannon Prince](https://www.linkedin.com/in/shannon-prince/)**, Independent Data Leader and Advisor, and **[Nathan Dhodapkar](https://www.linkedin.com/in/nathan-dhodapkar-968700b0/)**, Database Administrator at Connect for Health Colorado. Nathan works inside the state's official health insurance marketplace, where governance is a regulatory requirement rather than a best practice. Bring questions. AGENDA 5:30 to 6:15 · Networking and happy hour 6

  • Sep
    18
    2026

    Hands-on Practical AI Tips

    Fri, Sep 18, 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 at a NEW LOCATION on Thursday, Sept 17th, at 6:30 pm MT on “Hands-on Practical AI Tips.”** **Please note the new location below for our September meeting.** We expect to return to our usual CU Boulder location in October! **One aspect of AI is knowing how to use it effectively.** The biggest productivity gains often don't come from the next breakthrough model. They come from learning practical techniques, clever workflows, and small tips that save time every day. **Apply to demo: https://docs.google.com/forms/d/e/1FAIpQLSfN6b3LnMY6p6ppoCWQRxPisIXOZxNIUELyiobzZ3J8BHj5fw/viewform** **At our next meeting, we're turning the microphone over to our own community. Instead of a few long presentations, you'll hear a series of short, hands-on demonstrations from RMAIIG members who will share practical AI tips, favorite tools, and real-world workflows they've discovered.** Whether it's prompting, research, voice, automation, coding, images, video, writing, or AI agents, each presenter will teach something you can try yourself. Every demo represents a practical skill that can make you more productive, more creative, or simply help you get more value from AI. You may discover a new tool, a better workflow, or just one small technique that changes the way you work. The treasure isn't AI itself. It's knowing how to use it. You'll leave with a collection of practical ideas, actionable techniques, and new workflows you can put to work tomorrow morning. And perhaps even more importantly, you'll see how much there is to learn from one another as we navigate this rapidly changing landscape together. **A special thank you to 5-POINT LIFE Center Innovation Campus for generously providing our meeting space. Full details and link below!** The 5-POINT LIFE Center Innovation Campus is purpose-built for the next generation of AI, high-performance computing, and advanc

  • Sep
    29
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Sep 29, 2026 · 12:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

  • Oct
    13
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Oct 13, 2026 · 12:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

  • Oct
    13
    2026

    AI, Strategy & the Future of Work

    Tue, Oct 13, 2026 · 11:30 PM UTCDenver Data Dialogues

    Denver 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!

  • Oct
    27
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Oct 27, 2026 · 12:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

  • Nov
    10
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Nov 10, 2026 · 1:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

  • Nov
    24
    2026

    MARL Chapter 9.9: Population-Based Training

    Tue, Nov 24, 2026 · 1:30 AM UTCBoulder Data Science/ML/AI

    Boulder 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

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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