AI ML Engineering Visa Sponsorship Jobs in Colorado
Colorado's AI and ML engineering market is concentrated in Denver, Boulder, and the tech corridors along the Front Range, with employers like Google, Lockheed Martin, Palantir, and a growing base of funded startups actively hiring. International candidates with machine learning, deep learning, or MLOps backgrounds will find meaningful visa sponsorship activity here.
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Get Access To All JobsData Scientist
On-site in Longmont, Colorado. Monday through Friday, business hours, Mountain Time.
About Docketly
Docketly, a sister company to ABC Legal Services, is a fast-growing legal-tech company based in Longmont, Colorado. For the creditors' rights industry, we make hiring a stand-in attorney easy, fast, and reliable. We pair proprietary software with a nationwide network of attorneys.
Job Overview
You build and run the machine learning and analytics systems behind Docketly's operations. You build and run production models, from first idea to a live, monitored system on AWS. ABC Legal's data science team is your sounding board on methods. You build the BI reporting that leadership, operations, and client teams use daily. You support pricing and margin decisions with financial modeling. You work closely with backend engineers on data infrastructure and production issues. This role can grow into Senior Data Scientist as you take full ownership of the production model and the pricing roadmap.
Key Responsibilities
Model development and MLOps
- Build and run the full lifecycle of production ML models: framing, feature engineering, training, validation, deployment on AWS SageMaker (Model Registry, real-time endpoints), inference infrastructure (for example, AWS Lambda), and ongoing monitoring.
- Build and maintain automated retraining and drift monitoring for deployed models.
- Run staged production rollouts with holdout comparisons to prove model impact before full release. Review the test design with the team before launch.
- Use AI coding tools to speed up analysis, modeling, and MLOps work.
Analytics, pricing, and reporting
- Build and maintain the BI layer leadership relies on for exec reporting on revenue, margin, and volume, from dashboard design through data accuracy.
- Run pricing and margin analysis for client accounts, including ad hoc investigations for named strategic accounts. Model the financial impact of proposed pricing changes before they go live.
- Build and maintain rules-based pricing and case-difficulty logic by court and county, alongside statistical and ML approaches.
- Build and tune large scoring and prioritization frameworks in Metabase, such as weighting, percentile scenarios, and drop-rate analysis, that drive daily operational decisions such as Hearing priority and assignment.
- Turn ambiguous business questions into clear, well-scoped analyses.
Cross-functional partnership and documentation
- Partner with engineering on data pipelines (AWS Glue, Lambda, EventBridge). Investigate and help resolve production database performance issues, such as ORM query patterns and indexing, alongside backend engineering.
- Write down your methods and decisions in Confluence or an equivalent shared space, as you go, so other teams can find your reasoning and build on it without asking you directly.
Systems & Skills
Required
- Bachelor's degree in a related field, or equivalent practical experience, and 3+ years of experience with applied statistics or machine learning.
- Applied statistics: probability, hypothesis testing, and model evaluation across classification and regression/quantile models (for example, precision, recall, calibration, quantile coverage, and drift).
- 3+ years of experience with SQL and a relational database in a professional capacity. Docketly's production database runs on MySQL/Aurora, about 400 tables.
- Python for data analysis and modeling: pandas, scikit-learn, and a gradient-boosting framework (XGBoost, LightGBM, or CatBoost). Our production model uses CatBoost-based quantile regression.
- Experience training and deploying at least one model to production, or on a cloud ML platform such as AWS SageMaker.
- Metabase or comparable BI tooling.
- Clear written and verbal communication with non-technical stakeholders.
Preferred
- Staged production rollouts (canary or phased release) with holdout comparisons to prove model impact before full release.
- AWS Glue, S3, Lambda, EventBridge, CloudFormation.
- Comfortable using AI coding tools such as Claude Code or GitHub Copilot to move faster on analysis and modeling work.
- Experience diagnosing production database performance issues, such as query plans, indexing, and ORM-generated query patterns, in a large, high-table-count schema.
- Software-engineering practices applied to ML code, such as automated testing with pytest, for production pipelines.
Benefits
- Health, Dental, and Vision Insurance
- 401(k) with Company Matching
- Paid Time Off
- 7 Paid Company Holidays
- 4 Floating Holidays per Year
- Life Insurance and AD&D Insurance
- Long-Term Disability
- Health Care Reimbursement Flexible Spending Account (FSA)
- Dependent Care Flexible Spending Account
- Employee Assistance Program (EAP)
- Pet Insurance
Schedule & Location
Schedule: Monday-Friday, 8:00 AM-5:00 PM (Mountain Time) in Longmont, CO.
Salary Range: $110,000-$130,000 depending on experience
Application closing date: November 20, 2026, at 11:59pm
Data Scientist
On-site in Longmont, Colorado. Monday through Friday, business hours, Mountain Time.
About Docketly
Docketly, a sister company to ABC Legal Services, is a fast-growing legal-tech company based in Longmont, Colorado. For the creditors' rights industry, we make hiring a stand-in attorney easy, fast, and reliable. We pair proprietary software with a nationwide network of attorneys.
Job Overview
You build and run the machine learning and analytics systems behind Docketly's operations. You build and run production models, from first idea to a live, monitored system on AWS. ABC Legal's data science team is your sounding board on methods. You build the BI reporting that leadership, operations, and client teams use daily. You support pricing and margin decisions with financial modeling. You work closely with backend engineers on data infrastructure and production issues. This role can grow into Senior Data Scientist as you take full ownership of the production model and the pricing roadmap.
Key Responsibilities
Model development and MLOps
- Build and run the full lifecycle of production ML models: framing, feature engineering, training, validation, deployment on AWS SageMaker (Model Registry, real-time endpoints), inference infrastructure (for example, AWS Lambda), and ongoing monitoring.
- Build and maintain automated retraining and drift monitoring for deployed models.
- Run staged production rollouts with holdout comparisons to prove model impact before full release. Review the test design with the team before launch.
- Use AI coding tools to speed up analysis, modeling, and MLOps work.
Analytics, pricing, and reporting
- Build and maintain the BI layer leadership relies on for exec reporting on revenue, margin, and volume, from dashboard design through data accuracy.
- Run pricing and margin analysis for client accounts, including ad hoc investigations for named strategic accounts. Model the financial impact of proposed pricing changes before they go live.
- Build and maintain rules-based pricing and case-difficulty logic by court and county, alongside statistical and ML approaches.
- Build and tune large scoring and prioritization frameworks in Metabase, such as weighting, percentile scenarios, and drop-rate analysis, that drive daily operational decisions such as Hearing priority and assignment.
- Turn ambiguous business questions into clear, well-scoped analyses.
Cross-functional partnership and documentation
- Partner with engineering on data pipelines (AWS Glue, Lambda, EventBridge). Investigate and help resolve production database performance issues, such as ORM query patterns and indexing, alongside backend engineering.
- Write down your methods and decisions in Confluence or an equivalent shared space, as you go, so other teams can find your reasoning and build on it without asking you directly.
Systems & Skills
Required
- Bachelor's degree in a related field, or equivalent practical experience, and 3+ years of experience with applied statistics or machine learning.
- Applied statistics: probability, hypothesis testing, and model evaluation across classification and regression/quantile models (for example, precision, recall, calibration, quantile coverage, and drift).
- 3+ years of experience with SQL and a relational database in a professional capacity. Docketly's production database runs on MySQL/Aurora, about 400 tables.
- Python for data analysis and modeling: pandas, scikit-learn, and a gradient-boosting framework (XGBoost, LightGBM, or CatBoost). Our production model uses CatBoost-based quantile regression.
- Experience training and deploying at least one model to production, or on a cloud ML platform such as AWS SageMaker.
- Metabase or comparable BI tooling.
- Clear written and verbal communication with non-technical stakeholders.
Preferred
- Staged production rollouts (canary or phased release) with holdout comparisons to prove model impact before full release.
- AWS Glue, S3, Lambda, EventBridge, CloudFormation.
- Comfortable using AI coding tools such as Claude Code or GitHub Copilot to move faster on analysis and modeling work.
- Experience diagnosing production database performance issues, such as query plans, indexing, and ORM-generated query patterns, in a large, high-table-count schema.
- Software-engineering practices applied to ML code, such as automated testing with pytest, for production pipelines.
Benefits
- Health, Dental, and Vision Insurance
- 401(k) with Company Matching
- Paid Time Off
- 7 Paid Company Holidays
- 4 Floating Holidays per Year
- Life Insurance and AD&D Insurance
- Long-Term Disability
- Health Care Reimbursement Flexible Spending Account (FSA)
- Dependent Care Flexible Spending Account
- Employee Assistance Program (EAP)
- Pet Insurance
Schedule & Location
Schedule: Monday-Friday, 8:00 AM-5:00 PM (Mountain Time) in Longmont, CO.
Salary Range: $110,000-$130,000 depending on experience
Application closing date: November 20, 2026, at 11:59pm
AI ML Engineering Job Roles in Colorado
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Search AI ML Engineering Jobs in ColoradoAI ML Engineering Jobs in Colorado: Frequently Asked Questions
Which companies sponsor visas for AI ML engineers in Colorado?
Several Colorado employers have a documented history of sponsoring H-1B visas for AI and ML engineering roles. Google's Boulder office, Palantir Technologies in Denver, Lockheed Martin's tech divisions, and Arrow Electronics regularly appear in Department of Labor LCA filings for machine learning and AI-related positions. A growing number of well-funded Colorado startups in healthtech and aerospace also sponsor, though less consistently than larger enterprises.
Which visa types are most common for AI ML engineering roles in Colorado?
The H-1B is the most common visa for AI and ML engineers in Colorado, given that these roles typically require a bachelor's degree or higher in computer science, mathematics, or a related field, satisfying the specialty occupation standard. Candidates already in the U.S. on OPT or STEM OPT are frequently hired before employer-sponsored H-1B petitions are filed. The O-1A is an option for engineers with published research, patents, or significant professional recognition.
Which cities in Colorado have the most AI ML engineering sponsorship jobs?
Denver and Boulder account for the large majority of AI and ML engineering sponsorship activity in Colorado. Denver attracts enterprise and government contractors, while Boulder's proximity to the University of Colorado and a dense startup ecosystem drives demand for research-oriented ML talent. The Denver Tech Center suburban corridor also hosts mid-size tech companies with active engineering hiring. Colorado Springs sees some activity, primarily through defense and aerospace contractors.
How to find ai ml engineering visa sponsorship jobs in Colorado?
Migrate Mate filters job listings specifically by visa sponsorship availability, making it straightforward to find AI and ML engineering roles in Colorado without sorting through positions that won't support international candidates. You can search by location and role type to surface companies actively petitioning for these positions. Focusing on employers with prior LCA filings in Colorado for AI or ML job titles helps narrow your search to verified sponsors.
Are there state-specific considerations for AI ML engineers seeking sponsorship in Colorado?
Colorado's prevailing wage requirements under the H-1B program are benchmarked to Front Range metro areas, meaning employers in Denver and Boulder are typically required to offer wages aligned with the higher end of local market rates for ML and AI roles. Colorado also has a strong university pipeline through CU Boulder and Colorado State, which means OPT-to-H-1B transitions are a common hiring pattern that many local employers are already familiar with navigating.
What is the prevailing wage for sponsored ai ml engineering jobs in Colorado?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.