Top Machine Learning Engineer Jobs in Denver & Boulder, CO

Design, build, and operate cloud-native data and ML infrastructure for real-time and batch workloads. Implement reproducible training, evaluation, and inference pipelines; integrate event streams; monitor data quality, model performance, latency, and cost; automate deployments with IaC and CI/CD; collaborate with product, data science, and security teams; produce docs, dashboards, and runbooks.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

The role involves designing and building AI components for a platform, specifically focusing on NLP and ML for catalog mapping and anomaly detection, along with feedback mechanisms.

As a Staff Computer Vision AI/ML Engineer, you will lead a team to develop and deploy AI/ML algorithms in remote sensing, including analyzing datasets and training deep learning models.

Lead design, build, and operation of end-to-end production ML systems for ranking, rewards, ROAS/LTV prediction, personalization, and offer optimization. Own feature pipelines, training, inference, experimentation, monitoring, retraining, and MLOps. Partner with Data Engineering, BI, Product, and Engineering to connect ML to business outcomes, mentor engineers, and drive an AI-first engineering culture.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Develop, deploy, and optimize machine learning models (object detection, signal processing, predictive analytics) and data pipelines. Perform model evaluation, performance testing, and system-level debugging. Collaborate with engineers to integrate AI/ML into mission-critical systems, document designs, and support validation, verification, and safety/compliance activities.

Design, develop, and deploy advanced AI/ML models (deep learning, RL, generative) for aerospace and defense. Architect scalable systems, integrate with hardware, lead projects, validate and verify models, mentor junior engineers, and ensure compliance and safety for mission-critical applications.

Lead AI/ML technical vision and architecture across multiple programs, driving R&D in autonomy, perception, generative AI, and large-scale ML systems. Define standards, infrastructure, and MLOps practices; oversee end-to-end system design, validation, and deployment; mentor engineers and represent the team to leadership, customers, and partners.

Technical lead for rapid AI/ML prototyping in autonomy, perception, and analytics. Architect, build, deploy, and validate end-to-end ML systems (training, inference, deployment), mentor engineers, set technical direction, manage risks, and demonstrate mission-focused prototypes in aerospace and defense environments.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and deploy scalable AI/ML systems and pipelines. Develop reusable Python packages, RAG/LLM integrations, agentic workflows, evaluation/monitoring frameworks, optimized data queries, and MLOps practices while collaborating across cross-functional teams.

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 implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.

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.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

The Staff Machine Learning Engineer will lead ML initiatives, develop AI systems, optimize pipelines, and mentor team members to automate healthcare processes.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.