Top Machine Learning Jobs in Denver & Boulder, CO

Conduct research at the intersection of AI, cybersecurity, and computer systems. Formulate research questions, design experiments, build prototypes, evaluate AI and security methods, analyze real-world security data, and communicate findings through reports, presentations, and potential publications. The role combines research with practical engineering and offers mentorship while developing independence.

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.

Leads the enterprise Data Science and AI vision, strategy, roadmap, and global organization. Oversees production-grade machine learning, Generative AI, Agentic AI, predictive modeling, AI governance, deployment, monitoring, cost optimization, and responsible AI. Partners with executive, product, engineering, commercial, legal, operations, and customer success stakeholders to deliver scalable AI products, automation, improved customer outcomes, and measurable business value.

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.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead development, integration, testing, validation, and deployment of AI and machine-learning capabilities for radar, communications, and electronic warfare signal-processing applications. Own technical workstreams, develop mission-system algorithms, integrate models into complex software platforms, troubleshoot deployments, and collaborate with engineering, program, and government customer teams. The role requires expertise in signal processing, ML model development, evaluation, and deployment, with potential work involving multiple sensing modalities and mission-critical defense systems.

Leads the AI and Data practice for Energy Providers by driving business growth, cultivating utility executive and technology partner relationships, and originating complex consulting opportunities. Oversees strategic AI, data, analytics, and technology engagements from strategy through implementation, develops differentiated offerings, and advises clients on transformation roadmaps. Builds and manages multidisciplinary teams, oversees staffing and recruiting, develops senior talent, and aligns capabilities with market demand and emerging technology trends.

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Responsibilities include requirements analysis, software design, perception, planning, controls, human-machine interfaces, systems modeling, troubleshooting, debugging, and field testing. The role supports the full technology development lifecycle and requires familiarity with tactical networks, ATAK, TAK Server, and current AI and autonomy research.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities span requirements generation, software and hardware integration, troubleshooting, verification, human-machine interfaces, and technical documentation. The role also supports hardware prototyping, mechanical and electronics design, qualification testing, design reviews, supplier coordination, and program planning. Work may involve robotics, computer vision, signal processing, and multi-agent systems, with travel to remote locations up to 25% of the time.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Develop and demonstrate HP AI software solutions for defense customers through presales support, customized proof-of-concepts, technical presentations, training, documentation, and workflow design. Partner with sales, engineering, product teams, customers, and partners to translate business needs into AI/ML solutions, drive adoption, and achieve opportunity-conversion goals in secure and regulated environments.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Leads AI strategy, prototyping, integration, testing, and production transition for THAAD and future missile-defense systems. Develops AI-based fire control and missile guidance algorithms, creates AI roadmaps, accelerates software development, translates requirements into autonomous-system specifications, integrates hardware and software, and coordinates verification, validation, and system testing. Collaborates with customers, program teams, and engineering stakeholders while addressing technical, cost, and schedule considerations.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine-learning algorithms for mission systems. Translates requirements into software, builds perception, planning, controls, and human-machine interface capabilities, and supports the full development lifecycle. Responsibilities include model training, software integration, troubleshooting, debugging, test evaluation, and documentation. The role also collaborates across teams using Agile practices and supports mission-critical product and platform development.

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Responsibilities include perception, planning, controls, human-machine interfaces, requirements translation, software design, debugging, verification, and fielded product integration. The role may also involve deep learning, generative AI, MLOps, robotics, cloud ML platforms, safety-critical testing, and mentoring engineers.

Conduct exploratory data analysis, statistical research, and machine learning to identify patterns, build predictive models, and deliver actionable recommendations. Translate customer collection priorities into technical strategies, plans, and requirements. Analyze historical data, assess mission effectiveness, maintain traceability documentation, and liaise between customers and engineering teams. The role requires UNIX/Linux troubleshooting, strong communication, customer engagement, and an active TS/SCI clearance with CI polygraph.

Researches and develops AI, machine learning, computer vision, robotics, signal processing, and control algorithms for autonomous defense and commercial systems. Builds and tests prototypes, trains and optimizes models, defines algorithm requirements, and collaborates across perception, planning, simulation, and testing teams. The role also includes developing MLOps demonstrations, proof-of-concept environments, technical presentations, customer discovery support, RFP responses, and sales engineering enablement.

Leads customer-facing deployment of an internal AI/ML Ops platform, owning solution discovery, architecture, implementation, deployment, and adoption for MLOps and GenAI workflows. Identifies platform weaknesses and drives remediation with core engineering teams. Provides technical leadership, develops production AI/ML systems, and works across Kubernetes, GPU-backed, cloud, and on-premises environments while collaborating with customers and stakeholders.

Leads research and development of AI, machine learning, robotics, and autonomy technologies for defense and commercial applications. Directs technical teams, advances algorithms for perception, planning, control, and mission systems, and guides technologies from research through prototype development, integration, evaluation, and delivery. Builds customer relationships, shapes technical strategies and R&D programs, manages research portfolios, and communicates complex AI concepts to executives, scientists, customers, and program leaders.

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Translates requirements into software, supports perception, planning, controls, and human-machine interfaces, and troubleshoots deployed capabilities. Leads technical teams and guides AI/ML efforts from research or prototype stages through operational delivery, including technical strategy, integration, testing, risk management, and mentoring. Work may involve time-series, telemetry, sensor, anomaly detection, forecasting, and predictive modeling applications in aerospace or defense systems.