Top Machine Learning Engineer Jobs in Denver & Boulder, CO

Lead design, development, and deployment of production-grade, large-scale ML systems. Influence ML strategy, integrate models with platform and data infrastructure, mentor engineers, communicate results to stakeholders, and mature ML infrastructure and abstractions across teams.

Lead design, development, and deployment of large-scale production ML systems and platform strategy. Research and prototype ML and big-data technologies, integrate ML services across the org, mentor engineers and data scientists, and communicate complex ML solutions to technical and non-technical stakeholders.

Lead technical direction and delivery of ML platform infrastructure (feature store, model serving, experimentation, training pipelines). Drive architecture, resolve system-level issues, write production code, mentor engineers, and partner with ML, data, and product teams to enable scalable, reliable ML model development and deployment.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Build and ship computer-vision models end-to-end: dataset curation, training, deployment, and monitoring across cloud and roadside edge hardware. Optimize for high-volume inference, accuracy, efficiency, and real-world robustness while collaborating cross-functionally and iterating rapidly.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

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.

Build and ship production-scale computer vision models end-to-end: data collection, dataset curation, training, deployment (cloud and edge), monitoring, and iteration. Work with real roadside hardware and sensors to deliver efficient, accurate detection, segmentation, classification, embeddings, and re-identification models at scale.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

The Principal AI/ML Engineer will develop AI/ML solutions for hardware and firmware validation, enhance test efficiency, and collaborate across teams to implement data-driven analytics and automation.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

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.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Lead design, build, and production deployment of ML and GenAI solutions (LLMs, RAG, multi-agent systems, recommender and RL models). Architect scalable, secure inference and MLOps pipelines, mentor MLEs, collaborate with stakeholders to define roadmaps, and measure model performance and business impact. Implement fine-tuning, retrieval, orchestration, and monitoring for enterprise-grade AI products.

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, 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.

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 NLP and LLM-powered pipelines to extract, summarize, and semantically search technical documentation. Fine-tune transformer models, build RAG systems, convert unstructured documents into structured knowledge, optimize inference for production, and collaborate with engineers and SMEs to deploy document intelligence solutions.

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.

Develop and productionize consumer-facing AI features using LLMs. Collaborate across the AI stack, run A/B tests and evaluations, improve model reliability and guardrails, and optimize latency, cost, and scalability of production AI systems.