Top Machine Learning Jobs in Denver & Boulder, CO

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The role involves building and optimizing a machine learning and simulation platform, supporting MLOps, model deployment, and infrastructure modernization, while mentoring engineers.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

The role involves designing, building, and deploying computer vision models for vehicle damage detection, covering the entire ML lifecycle and collaborating across teams.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

The Principal Machine Learning Engineer will lead ML initiatives, integrate ML services, mentor engineers, and drive ML strategy within Ibotta, ensuring scalable solutions are implemented effectively across teams.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

Lead architecture and development of a SaaS platform for data center energy procurement. Build forecasting, contracting, and monitoring modules; implement AI tools on AWS (Bedrock, SageMaker); hire and mentor engineers; translate business logic into scalable systems; deliver demos to investors and customers.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Lead the Machine Learning Operations team to develop a robust ML platform, manage projects, and enhance AI tooling integration while driving engineering excellence.

The Lead Bioinformatics AI Scientist will lead AI and GenAI R&D initiatives to develop advanced clinical testing and genomic data analysis platforms, leveraging expertise in bioinformatics and machine learning.

The SVP of AI Innovation - Web Scraping will lead the development of AI-driven web scraping strategies, focusing on data acquisition, technology enablement, compliance, team building, and cross-functional collaboration for new data services.

The AI Application Engineer will design and deploy AI systems, collaborate with teams, implement Generative AI solutions, and promote continuous learning in AI practices.

The role involves leading a team of Forward-Deployed Machine Learning Engineers to deliver AI solutions, manage project execution, and enhance operational excellence.

Lead the creation and optimization of machine learning algorithms, develop and refine production-grade models, and drive testing of innovative algorithms using historical data.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.