Top Remote Senior Level Machine Learning Engineer Jobs in CO

Build, fine-tune, evaluate, and deploy computer vision and multimodal machine learning models in production. Develop scalable data pipelines, evaluation frameworks, real-time processing systems, and cloud infrastructure with monitoring and automated scaling. Improve image processing, barcode decoding, and OCR capabilities while partnering with engineering and fulfillment teams. Contribute through code reviews, documentation, testing, technical leadership, and mentorship.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

Owns the infrastructure and production lifecycle for product and marketing machine learning models. Responsibilities include real-time model deployment, feature pipelines and stores, monitoring and alerting, CI/CD, retraining infrastructure, marketing model operationalization, data pipelines, ad-platform feedback loops, and dependency management. The role bridges data science and engineering while mentoring teammates and making pragmatic architecture decisions.

Lead the Machine Learning Ranking team, managing data scientists and ML engineers while driving career development, technical quality, and product outcomes. Partner with Product Management and Decision Science to define roadmaps, metrics, and marketplace experiments. Oversee machine-learning pipeline development, model deployment, and performance monitoring. Provide technical guidance on ML and AI applications, evaluate engineering work, and promote scalable, reproducible practices across the team.

Leads development of production-scale machine learning and reinforcement learning systems for Humana’s Next Best Action platform. Designs decision policies, simulations, offline evaluations, and monitoring strategies; builds scalable pipelines using Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow. Owns models throughout their lifecycle and provides technical direction on architecture, experimentation, deployment, and constraint-aware decision-making.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Develops data pipelines and AI adoption analytics infrastructure for enterprise-wide AI transformation. Builds ETL/ELT workflows, AI/ML data models, quality controls, governance processes, and dashboards using Python, SQL, and orchestration tools. Integrates data from enterprise and AI platforms, supports secure on-premises and classified environments, and translates adoption metrics into executive reporting. Collaborates across engineering, product, cybersecurity, and leadership teams while contributing reusable components and mentoring junior staff.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

Develop algorithms and high-performance systems across the GenAI lifecycle, including multimodal data preparation, distributed foundation-model training, reinforcement-learning post-training, inference optimization, and open-source AI infrastructure. The role involves improving scalability, efficiency, deployment cost, and model support while collaborating with research, product, and infrastructure teams.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.