Top Remote Entry Level Machine Learning Engineer Jobs in CO

Build, train, and productionize machine learning models for fraud detection and identity verification. Perform data acquisition, feature engineering, experimentation, monitoring, and analyses to inform product, sales, and risk decisions. Collaborate with engineering, data acquisitions, and risk operations to maintain data quality and ship production-ready code. Research new fraud types and contribute to new product development across the ML lifecycle.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Deploy and optimize frontier AI models for real-time production use at scale. Responsibilities include improving inference latency, throughput, and cost through quantization, distillation, KV-cache optimization, batching, and custom kernels; building high-performance streaming serving systems; and creating tooling that enables safe, efficient model deployment.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

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.

Six-month remote AI/ML trainee program focused on developing Python and database skills, understanding complex code, and exploring LLM, vector database, knowledge graph, and open-source technologies. Trainees participate in global team meetings through Zoom and collaborate using Slack, Jira, and WhatsApp. The role includes a two-week in-person sprint at an offsite location. A B.Tech or MCA degree and strong self-learning ability are required.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and model release gates. Run end-to-end training experiments, select open-weight models, improve tool-using and multi-turn behavior, deploy models to production, and iterate using real-world usage. The role emphasizes Python engineering, reinforcement learning, GPU-based training, data quality, reward design, and production machine learning.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.