Top Senior Machine Learning Jobs in Denver & Boulder, CO

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Owns end-to-end development of datasets, reinforcement learning environments, and evaluations for coding agents and software engineering models. Leads engineers, researchers, QA professionals, and data creators; establishes quality processes, pipelines, validation, and review workflows. Collaborates directly with frontier AI labs, fine-tunes models, builds benchmarks, develops automation and internal tools, and supports customer-facing enablement. The role combines hands-on technical leadership, engineering management, operational execution, research, and industry thought leadership.

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Lead the architecture and delivery of scalable machine learning systems, including real-time model serving, MLOps, monitoring, governance, and automated deployment. Own the AI/ML integration connecting Xometry with partner design and manufacturing platforms. Develop cloud-based production systems, optimize model latency and throughput, establish technical standards, mentor engineers, and collaborate with engineering, product, data science, and business teams.

Technical leader responsible for major cross-functional ML and AI initiatives, including long-term technical roadmaps, scalable production systems, generative AI, computer vision, and 2D/3D modeling. The role drives business impact through revenue growth, cost reduction, efficiency improvements, and competitive differentiation. Responsibilities include architecture, MLOps, model governance, infrastructure decisions, stakeholder influence, team mentorship, and translating emerging technologies into manufacturing applications.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

The Senior Machine Learning Researcher will design experiments, research new algorithms, and collaborate on deploying models for computer vision and robotics applications.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Leads the design, development, deployment, and validation of advanced machine learning systems for aerospace and defense applications. Responsibilities include developing deep learning, reinforcement learning, generative AI, computer vision, signal processing, and planning algorithms; architecting scalable CPU/GPU platforms; managing projects; mentoring engineers; and ensuring safety, performance, cybersecurity, and regulatory compliance. The role involves occasional travel and requires the ability to obtain and maintain a Secret U.S. security clearance.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Lead the architecture and production deployment of agentic AI systems for classified and mission-critical government environments. Build systems for geospatial reasoning, retrieval, embeddings, persistent memory, orchestration, evaluation, and reliability. Own shared ML infrastructure, define technical direction, partner across engineering and research teams, mentor engineers, and drive ambiguous initiatives from prototype to supported production capability.