Top Remote Machine Learning Engineer Jobs in Denver & Boulder, CO

Lead design, training, fine-tuning, evaluation, and production deployment of TTS/voice-generation models. Translate research into reliable, low-latency production systems, define ML best practices, and collaborate across research, engineering, and product to drive execution.

Build and deploy production ML models and end-to-end pipelines to detect suspicious behavior; design evaluation, monitoring, and drift detection; integrate intelligence into product workflows; improve data quality and accessibility; and contribute to operational rigor, runbooks, and safe rollout practices in a security-sensitive environment.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

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.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Lead technical direction and hands-on development of ML and AI Context systems for HubSpot CRM: model development, productionization, retrieval, embeddings, evaluation, monitoring, and cross-team architectural leadership to deliver measurable product and business impact.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Design and build scalable training and inference systems for LLMs and multimodal/media models. Optimize end-to-end training (data pipelines, distributed training, mixed precision) and inference (KV cache, batching, quantization). Scale models into production, lead efficiency initiatives, and mentor engineers and scientists to improve latency, scalability, and reliability.

Looking for a Machine Learning Engineer to enhance AI capabilities in image analysis and structured data. Responsibilities include developing computer vision models, deploying ML systems, and collaborating with engineering teams.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Design, build, and operate cloud-based data pipelines, data lakes, and ML systems on AWS. Optimize databases and ingestion for structured and unstructured data, develop and deploy ML models using Jupyter and AWS AI/ML services, and support customers and internal teams on data architecture and migrations.

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.