Top Remote Senior Level Machine Learning Engineer Jobs in CO

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.

Research, design, and implement discrete optimization and mission-planning algorithms for multi-agent autonomous systems; improve and maintain C++ mission-planning software; define technical roadmaps; mentor engineers; perform full software and hardware-in-the-loop simulations.

Lead end-to-end AI product discovery, prototype and ship working ML/AI solutions for HR, Marketing, and Commercial teams. Build first prototypes yourself, partner with engineers to productionize, define success metrics, and drive adoption and business impact.

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.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Build and maintain data models, ETL pipelines, and production machine learning systems supporting recommendations, personalization, pricing, search, and marketplace optimization. Own features end to end, conduct statistically rigorous experimentation, monitor deployments, write technical designs, and collaborate with engineering, data science, and product teams to deliver measurable business impact.

Designs, builds, and owns production RAG systems, including ingestion, chunking, embeddings, hybrid retrieval, reranking, evaluation, secure multi-tenant access, and LLM serving. Optimizes open-source models with Ollama and llama.cpp across cloud, on-premises, and air-gapped environments. Establishes architecture standards, reviews designs, mentors AI developers, evaluates emerging RAG techniques, and supports Azure/AWS infrastructure, Kubernetes deployments, CI/CD, observability, and Kafka-based ingestion pipelines.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, and model benchmarking, routing, and fallback strategies. Develop scalable backend and AI platform services for LLM deployment, orchestration, monitoring, and evaluation. Integrate AI features into core products while ensuring reliability, security, privacy, compliance, and cost-effective performance. Collaborate with product, frontend, data, and engineering teams to deliver customer-facing AI capabilities.