Top Machine Learning Engineer Jobs in Denver & Boulder, CO

The Principal Machine Learning Engineer will fine-tune and optimize LLMs for various applications, develop AI products, and enhance ML infrastructure in collaboration with cross-functional teams.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Design, build, and maintain scalable ML models and data pipelines to automate logistics processes. Own end-to-end projects from problem definition to production deployment and monitoring. Collaborate with engineering, mentor ML teammates, review code, and evaluate tooling to improve infrastructure and workflows that deliver customer value.

Develop, fine-tune, and deploy LLMs for NLP tasks; design and maintain large-scale data pipelines; analyze time-series data; implement data-driven models and OCR systems; ensure data quality; collaborate cross-functionally; and research and apply best practices to improve internal data processing tools and infrastructure.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Design and build AI assistants for interactive 3D workflows: build NLP pipelines, integrate cloud and client LLMs, design knowledge graphs, fine-tune foundation models, prototype AI features, and improve personalization and system performance.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

Design, develop, and deploy advanced AI/ML systems (deep learning, reinforcement learning, generative models) for aerospace and defense. Architect scalable solutions, integrate with hardware/software, lead projects, validate and verify performance and safety, mentor junior engineers, and collaborate with cross-functional teams to deliver mission-critical autonomous, sensor-fusion, and real-time decision-making capabilities.

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

The Staff AI/ML Engineer will develop and prototype AI/ML algorithms, analyze datasets, and mentor team members in an interdisciplinary environment.

Lead development and implementation of AI-driven solutions focused on text classification and NLP. Build and optimize ML models, preprocess and transform text data, combine supervised/active/semi-supervised approaches, create tagged datasets, and provide technical leadership while collaborating cross-functionally to align AI capabilities with product and business goals.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

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.

Build and maintain data and model layers for an AI decision‑support system: data ingestion, probabilistic record matching, calibrated scoring, provenance‑tracked retrieval and grounded generation, and secure production pipelines. Ensure explainability, auditability, and accredited deployment in a controlled government cloud environment.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Build, deploy, and operate LLM-powered agents: design tool use and orchestration, monitor production health, evaluate with test suites, implement retrieval/RAG, build integrations and scalable AWS infrastructure with Terraform and CI/CD, enforce guardrails and observability, document architectures, and expand agent adoption across the organization.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

Lead design, build, and operation of end-to-end production ML systems (feature pipelines, training, inference, experimentation, monitoring) for ranking, recommendation, rewards, ROAS/LTV prediction, personalization, and offer optimization in a high-scale AdTech environment. Provide hands-on technical leadership, mentor engineers, drive MLOps and observability standards, and partner with Data Engineering, BI, Product, and business teams to connect models to outcomes.