Complete funding guide for machine learning entrepreneurs in Oregon. Find 7 VC funds, 6 accelerators,4 angel networks, and 2 grant opportunities.
The machine learning startup ecosystem in Oregon has experienced remarkable growth, with $426M in funding across 118 deals in 2026.This represents 27% year-over-year growth compared to the previous year, positioning Oregonas a emerging market formachine learning innovation.
The average funding round size of $6M reflects strong investor confidence in Oregon-based machine learning startups. Competition levels are currently moderate, with 7 active VC funds specifically targeting this sector.
Oregon offers a concentrated machine learning ecosystem with specialized talent
Strong government support for machine learning innovation and R&D tax incentives
Access to leading universities and research institutions in the region
Lower operational costs compared to traditional tech hubs like San Francisco
series-a stage focus • 41 portfolio companies
seed stage focus • 27 portfolio companies
series-a stage focus • 65 portfolio companies
seed stage focus • 22 portfolio companies
series-a stage focus • 39 portfolio companies
series-a stage focus • 22 portfolio companies
series-b stage focus • 67 portfolio companies
📍 Oregon
📍 Oregon
📍 Oregon
📍 Oregon
Strong funding activity as VCs deploy fresh capital from annual fund raises
Peak investment period with increased deal velocity and accelerator demo days
Moderate activity as partners focus on portfolio company support during summer
Year-end push to deploy remaining capital and close pending deals
Machine Learning startup funding in Oregon is moderate, with 7 active VC funds and 6 accelerator programs competing for deals. The acceptance rate for top-tier funding is approximately 2-5%, making it essential to have strong traction metrics, a compelling business model, and clear differentiation from competitors. Success factors includeDeep expertise in machine learning domain, Strong technical team with proven track record, Clear market validation and customer traction.
Machine Learning startups in Oregon raise an average of $6M per funding round, based on 118 deals completed in 2026. This represents 27% year-over-year growthcompared to the previous year. Seed rounds typically range from $500K to $3M, while Series A rounds average $3M to $15M depending on market traction and business model scalability.
Top machine learning accelerators in Oregon report success rates of 75-85% for follow-on funding, with program lengths ranging from 12-16 weeks and equity terms of 4-7%. The most successful programs focus onmachine-learning and startup-growthand provide extensive mentor networks, investor connections, and post-graduation support lasting 2+ years.
Oregon offers several unique advantages for machine learning startups: Oregon offers a concentrated machine learning ecosystem with specialized talent, Strong government support for machine learning innovation and R&D tax incentives, and Access to leading universities and research institutions in the region. The local ecosystem includes 7 specialized VC funds, 4 angel networks, and 2 grant programs specifically supporting machine learning innovation.
The optimal fundraising months in Oregon are February, March, May, when investor activity peaks and deal velocity increases. Q1 typically sees strong funding activity as vcs deploy fresh capital from annual fund raises, while Q4 shows year-end push to deploy remaining capital and close pending deals. Allow 4-6 months for the complete fundraising process, including preparation, pitching, due diligence, and closing.
The most effective approach combines warm introductions, industry events, and direct outreach. Start by leveraging your network for introductions to the 7 active VC funds in Oregon. Join relevant machine learning meetups, attend pitch competitions, and participate in accelerator demo days. Angel networks like Oregon Machine Learning Angels host regular investor meetings and can provide valuable early-stage funding and mentorship.
Last updated: 3/16/2026 | Data aggregated from 7 VC funds, 6 accelerators, and 4 angel networks |About our methodology