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Ml Infrastructure Jobs in Colorado (NOW HIRING)

Paradigm

Senior ML Ops Engineer

Denver, CO · On-site

$123K - $170K/yr

... infrastructure, and AI-powered innovation. What You Will Do: · Design, build, and maintain ... ML deployment pipelines, ensuring reliable, reproducible, and efficient model delivery to ...

AppFolio

Staff Machine Learning Engineer

Denver, CO · On-site

$200 - $250/hr

Design and operate AppFolio's ML infrastructure on AWS, including ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls. * Optimize AI cost across all applications through routing ...

AppFolio

CO · On-site

This role works at the intersection of ML infrastructure, applied AI, and cost discipline. You'll partner closely with our Voice & Agents and Research ML engineers to harden their prototypes into ...

AppFolio

Staff Machine Learning Engineer - Leasing

Denver, CO · On-site

$17.50 - $20.50/hr

Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence -- defining what "better" looks like for leasing-specific tasks and owning the ...

AppFolio

CO · On-site

$17.50 - $20.50/hr

Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence -- defining what "better" looks like for leasing-specific tasks and owning the ...

Nametag

Senior Software Engineer, Reliability

Denver, CO · Remote

$120K - $190K/yr

Data & ML Infrastructure * Design and operate data pipelines that support our ML-powered verification systems. * Evolve our MLOps infrastructure so models can be trained, evaluated, and deployed ...

Socket.dev

Software Engineer, ML Platform

Denver, CO · On-site

$160 - $240/hr

Gusto is looking for a strong Machine Learning Platform and Infrastructure Engineer to join our ML Platform team and build out and scale our ML and AI platform. As a Machine Learning Platform ...

Gusto, Inc.

Software Engineer, ML Platform

Denver, CO · On-site +1

$190K - $240K/yr

Gusto is looking for a strong Machine Learning Platform and Infrastructure Engineer to join our ML Platform team and build out and scale our ML and AI platform. As a Machine Learning Platform ...

Compa

Senior Software Engineer (all teams)

Denver, CO · On-site

$120 - $160/hr

Interest or experience in AI systems or ML infrastructure * Comfortable with ambiguity and fast, iterative work Tech Stack You don't need experience with all of these, but familiarity with any is a ...

Compa

Senior Software Engineer (all teams)

Denver, CO · On-site

$165K - $220K/yr

Interest or experience in AI systems or ML infrastructure * Comfortable with ambiguity and fast, iterative work Tech Stack You don't need experience with all of these, but familiarity with any is a ...

Drive Capital

Senior Data/ML Engineer

Denver, CO · On-site

$120 - $150/hr

We operate at the intersection of fintech, data infrastructure, and real-time decisioning, where ... This role spans data engineering, ML engineering, and MLOps, with responsibility for building a ...

Sierra Nevada Corporation

AI/ML Engineer III

Englewood, CO · On-site

$124.77 - $171.56/hr

As an AI/ML Engineer III, you will be the primary driver for AI/ML projects focused on autonomy ... Provide guidance on selecting tools, frameworks, and infrastructure. * Translate high‑level ...

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Ml Infrastructure information

What is ML infrastructure?

ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.

What are some common challenges faced by professionals working in ML infrastructure roles?

Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.

What are the key skills and qualifications needed to thrive as an ML infrastructure engineer, and why are they important?

To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.

What is the difference between Ml Infrastructure vs Data Engineer?

AspectML InfrastructureData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; knowledge of cloud platformsBachelor's in CS, Software Engineering, or related; experience with databases and ETL tools
Work EnvironmentFocus on deploying and maintaining ML systems, cloud environments, and infrastructure toolsDesigning, building, and managing data pipelines and storage solutions
Industry UsageUsed in AI/ML teams to support model deployment and scalabilityUsed across data-driven organizations for data management and analytics

ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.

What are popular job titles related to Ml Infrastructure jobs in Colorado?

For Ml Infrastructure jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Ml Infrastructure jobs in Colorado look for?

The top searched job categories for Ml Infrastructure jobs in Colorado are:

Ml Infrastructure jobs near you
Infographic showing various Ml Infrastructure job openings in Colorado as of August 2026, with employment types broken down into 88% Full Time, 8% Part Time, and 4% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Senior ML Ops Engineer

Denver, CO • On-site

Paradigm
See more jobs at Paradigmexternal

$123K - $170K/yr

Full-time

Re-posted 13 days ago

Job description

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are building the future with modern software engineering, agent-assisted systems, and mobile-first experiences. We are powered by our parent company, Builders FirstSource (NYSE: BLDR): a Fortune 300 company with over $23 billion in revenue and more than 29,000 employees across 550+ locations, BFS is redefining construction through data, digital infrastructure, and AI-powered innovation.

What You Will Do:

· Design, build, and maintain scalable MLOps solutions that support the end-to-end machine learning lifecycle, including model training, deployment, monitoring, and retraining.

· Develop and optimize automated ML deployment pipelines, ensuring reliable, reproducible, and efficient model delivery to production environments.

· Deploy and support machine learning models and AI solutions in production, maintaining best practices for scalability, reliability, security, and operational excellence.

· Implement and maintain model registries, experiment tracking, versioning, and governance practices to support consistent model lifecycle management.

· Build and support containerized ML workloads and deployment workflows using technologies such as Docker and Kubernetes.

· Develop monitoring, observability, and alerting capabilities for machine learning systems, including model performance tracking, drift detection, and data quality monitoring.

· Collaborate with Machine Learning Engineers, Data Scientists, Software Engineers, and Infrastructure teams to operationalize ML solutions and improve deployment efficiency.

· Implement and maintain IaC patterns using Terraform.

· Troubleshoot and resolve complex technical challenges related to model deployment, ML infrastructure, and production operations.

· Provide guidance and mentorship to other engineers.

What You Need to Succeed:

· Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence or related field or equivalent experience.

· 4+ years of professional experience in software engineering, machine learning engineering, MLOps, platform engineering, DevOps, or a related technical discipline.

· Strong understanding of the machine learning lifecycle, including model training, validation, deployment, monitoring, and retraining.

· Experience building and maintaining automated machine learning pipelines and CI/CD workflows.

· Experience with MLOps platforms and tools such as MLflow, Kubeflow, Azure Machine Learning, Databricks, or similar technologies.

· Experience in Python programming, ML Framework and Agentic AI. Implemented model monitoring, experiment tracking, model versioning, and governance practices.

· Experience working with cloud-based machine learning solutions, preferably within Azure.

· Ability to independently solve complex technical challenges, make sound decisions with minimal guidance, and drive work to completion.

Ready to Join? Apply now at myparadigm.com/careers/

Compensation Range: $123K - $170K