Top Machine Learning Jobs in Denver & Boulder, CO

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

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.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Develop production-quality computer vision, image processing, machine learning, and geospatial algorithms for imagery and spatial data. Responsibilities include mathematical analysis, algorithm design and validation, software development, large-dataset analysis, system optimization, technical collaboration, code reviews, architecture discussions, and transitioning research prototypes into scalable AI-enabled workflows.

Build and deploy AI solutions for Retail Wireless FP&A, including LLM workflows, AI agents, anomaly detection, predictive analytics, and automated financial validation. Improve P&L modeling, variance analysis, forecasting, and scenario planning while partnering with Data Engineering on secure enterprise pipelines. Train FP&A staff in prompt engineering and AI agent workflows, ensuring outputs remain accurate, traceable, and subject to human oversight.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

Leads complex data science and AI initiatives, developing and deploying machine-learning solutions centered on LLMs, agentic workflows, and RAG systems. Translates stakeholder needs into technical roadmaps, mentors analysts and data scientists, manages project budgets and schedules, ensures quality, and designs reliable AI applications using embeddings, vector databases, retrieval systems, and AI workflow platforms.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

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.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Leads enterprise AI solution architecture across healthcare, defining scalable patterns for generative AI, agentic AI, machine learning, intelligent automation, and cloud platforms. Establishes governance, responsible AI controls, reference architectures, and technology standards while guiding engineering teams through production deployments. Partners with executives, product leaders, data scientists, security, legal, and compliance teams; evaluates emerging technologies; prototypes AI solutions; and mentors architects and engineers. Requires deep experience with LLMs, RAG, AI agents, vector databases, healthcare systems, distributed architectures, and enterprise technology governance.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Design, build, deploy, and operate production AI agents and multi-agent systems for insurance workflows. Responsibilities include agent orchestration, APIs, RAG pipelines, evaluation frameworks, regression testing, observability, production troubleshooting, and reliability improvements. The role requires hands-on Python software engineering, LLM application development, cloud infrastructure, and collaboration in end-to-end product pods.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Leads expeditionary AI discovery, assessment, prototyping, and delivery for government customers. Builds generative AI systems using LLMs, RAG, agentic workflows, and applied machine learning; integrates models with APIs, data services, cloud infrastructure, and testing. Engages stakeholders, translates requirements into technical plans, briefs technical and senior audiences, estimates staffing and effort, and guides prototypes toward operational deployment. The role also shapes AI roadmaps, transformation frameworks, team formation, and customer strategies.

Build and operate a scalable LLM inference platform integrated with the MAX stack. Develop distributed, multi-node model deployments, high-throughput batching, KV-cache management, autoscaling, observability, cold-start optimizations, and multi-cloud capabilities. Create Helm charts and Kubernetes operators while collaborating with kernel and generative AI teams to optimize performance. The role requires backend engineering, Kubernetes operations, machine learning experience, and distributed systems expertise.

Provides technical leadership for an Engineering Generative AI portfolio. Defines architectures, roadmaps, reusable patterns, and secure full-stack solutions spanning applications, APIs, services, data, AI models, and deployment pipelines. Guides prototypes into enterprise-ready systems, evaluates emerging technologies, embeds responsible AI governance, and designs human-centered agentic workflows. Partners across engineering, IT, data, cybersecurity, vendors, and leadership while mentoring technical teams.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Develops data science, machine learning, and generative AI solutions for customer engagements and pre-sales opportunities. Translates ambiguous business challenges into analytical use cases, prototypes, hypotheses, demonstrations, and actionable recommendations. Explores customer data, applies statistical and advanced analytical techniques, communicates findings to technical and business audiences, and creates reusable analytics assets. Partners across Microsoft teams to connect customer data and AI opportunities with broader transformation priorities while applying responsible AI, security, governance, and data quality principles.

Leads teams developing, integrating, and deploying AI/ML capabilities for radar, communications, electronic warfare, and other sensor-level applications. Establishes technical roadmaps, manages staffing and workloads, mentors engineers, supports proposals and customer engagements, and collaborates with program, systems engineering, software, and customer teams. The role requires expertise in machine learning, signal processing, sensing systems, mission-critical software integration, and technical leadership.

Conduct machine learning and data science research for drug discovery, focusing on mass spectrometry, computational biology, and computational chemistry. Own projects from scientific question through results, develop data-driven solutions, validate AI-generated analyses, and communicate findings through visualizations and writing. Collaborate with the Data Science team and contribute to applications of AI in medicine development.