Top Machine Learning Engineer Jobs in Denver & Boulder, CO

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Lead the design and development of production-grade machine learning systems, including large-scale document pipelines and agentic workflows for healthcare claims and payment integrity. Drive LLM prompt engineering, multimodal AI, RAG, orchestration, observability, reliability, and explainability. Collaborate with data science, data engineering, and product teams, reduce technical debt, improve scalability, and mentor engineers on enterprise AI system design and development.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Own the ML platform layer connecting data science and infrastructure. Responsibilities include managing the feature store, building self-service training and deployment platforms, standardizing retraining, scaling batch and online inference, implementing monitoring, and establishing standards for model and feature versioning, lineage, testing, and documentation. The role partners closely with data scientists and backend engineers to improve model delivery speed, reliability, and independence across multiple teams.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Owns the infrastructure and production lifecycle for product and marketing machine learning models. Responsibilities include real-time model deployment, feature pipelines and stores, monitoring and alerting, CI/CD, retraining infrastructure, marketing model operationalization, data pipelines, ad-platform feedback loops, and dependency management. The role bridges data science and engineering while mentoring teammates and making pragmatic architecture decisions.

Develop and deliver AI/ML-enabled enterprise, cloud, and application software for national security space programs. Support software design, development, integration, testing, mission management, command and control, and data analytics. Collaborate with engineering teams on mission-focused capabilities using object-oriented programming, AI prompting, model training, evaluation, data preparation, Agile practices, unit testing, and configuration management.

Develop and deliver AI/ML, enterprise, cloud, and application-based software for national security space programs. Support software design, development, integration, testing, data analytics, mission management, and command-and-control capabilities. Collaborate with engineering teams, apply coding standards, perform unit testing, and use configuration management and Agile practices. The role requires familiarity with object-oriented programming, AI/ML concepts, Linux or UNIX, and software development lifecycle activities.

Researches, designs, develops, trains, and deploys computer vision and machine learning models. Builds and maintains ML software, testing and validation processes, and documentation. Applies image and video processing, deep learning, and computer vision techniques across edge and cloud-based platforms while improving existing applications and performance.

Build production AI/ML software for supercomputers and AI infrastructure, including anomaly detection, forecasting, root cause analysis, automation, and operational intelligence. Develop services with Go, Python, and React; integrate machine learning models, LLM workflows, agentic systems, and analytics. Own the lifecycle from data engineering and model development through deployment, monitoring, and improvement while collaborating with engineering, architecture, product, and customers.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Develop and deploy machine learning algorithms for defense and commercial applications, working with images, text, graph data, NLP, computer vision, reinforcement learning, and large language models. Responsibilities include analyzing large datasets, building inference models, using cloud services for training and deployment, researching emerging methods, and preparing technical documentation. The role requires Python, machine learning experience, a relevant bachelor’s degree, at least two years of experience, and an active TS/SCI clearance with CI Poly.

Develop and implement AI/ML algorithms across computer vision, NLP, time series, reinforcement learning, and large language models. Analyze multimodal datasets, apply research findings, document systems, support AWS-based training and deployment pipelines, and mentor team members. The role requires substantial AI/ML development experience, advanced Python and machine learning library proficiency, deep learning expertise, and an active TS/SCI clearance.

Leads researchers and developers in building computer vision AI/ML solutions for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include preparing datasets, researching supervised and unsupervised methods, training and optimizing deep learning models, deploying algorithms to cloud environments, communicating technical findings, and documenting systems. The role requires active TS/SCI clearance and provides technical leadership from prototype through operational deployment.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Build and scale pipelines that transform conversation data into structured customer memory. Develop LLM extraction and retrieval systems using embeddings, vector search, and ranking; create evaluation benchmarks and regression gates; diagnose production issues through logs, metrics, and traces; and partner with Product and ML teams to improve customer-focused AI systems.

Build and optimize scalable pre-training pipelines for foundation models. Implement distributed training across GPUs, TPUs, and high-performance clusters; translate research experiments into production workflows; develop monitoring and fault-tolerance systems; and benchmark and tune performance across hardware and software stacks.

Develop AI agents, chatbots, and machine learning models for a modernized federal data science platform. Research and standardize AI capabilities in Databricks and Palantir, prompt-engineer LLMs, integrate AI into DevOps pipelines, and train models on production datasets for prediction and anomaly detection. The role requires collaboration with developers and customers, database familiarity, Agile experience, documentation, and an active or obtainable DoD Secret clearance.

Design, develop, evaluate, and refine machine learning models using Python. Manage training data with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and deliver data-driven insights. Collaborate remotely with cross-functional contributors and support deployment or operationalization of models in cloud or enterprise environments.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.