Top Remote Machine Learning Jobs in Raleigh, NC

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Design, implement, and own end-to-end scalable ML systems and MLOps lifecycle (deployment, monitoring, metrics). Optimize models and system performance, work with Java/Python and TF/PyTorch, use CI/CD, Terraform, Docker/Kubernetes, lead inter-team communication, document work, and meet deadlines.

Lead design, build, and maintain scalable ML infrastructure and CI/CD pipelines. Collaborate with data science and operations teams to deploy containerized models (Docker/Kubernetes), optimize cloud resource usage and security, apply IaC (Terraform), and mentor junior engineers.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Design, build, and deploy scalable AI/ML systems and pipelines. Develop reusable Python packages, RAG/LLM integrations, agentic workflows, evaluation/monitoring frameworks, optimized data queries, and MLOps practices while collaborating across cross-functional teams.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Design, build, and own production ML and LLM-powered product features (chat, RAG, semantic search, data extraction). Write maintainable code and docs, debug issues, collaborate with product and design, follow Agile practices, and stay current on AI/ML methods.

Own and improve recommendation and search systems across feed, discovery, and search. Build retrieval and ranking (candidate generation, embeddings, two-tower models), run experiments, create user/content/session representations, and ship production ML systems with monitoring to improve retention, engagement, and content distribution.

Design, develop, and maintain scalable ML and AI platform infrastructure for model training, deployment, feature engineering, candidate generation, agent deployment, and observability. Collaborate with data scientists and engineers, support platform operations, drive improvements, codify best practices, and influence platform investments to enable production ML systems at scale.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

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

The role involves building, deploying, and maintaining ML infrastructure, optimizing systems, and ensuring production reliability while collaborating with data scientists and product teams.

Lead pre-sales technical engagements for Conversational and Generative AI solutions, collaborate across teams, and design value-driven demos for enterprise customers.

Manage the development and implementation of Evio Compass, an analytic product identifying patient risks, while guiding cross-functional teams to drive customer success and enhance ML/AI product features.

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

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

The Technical Product Manager will develop and manage the cloud infrastructure for AI, collaborating across teams to enhance AI solutions and service offerings.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.

Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos) for AI/ML and GPU workloads. Own control plane and etcd lifecycle, build controllers/operators in Go, automate via IaC, improve observability and resource utilization, enable ML training/inference/LLM deployments, participate in on-call incident response, and mentor engineers.

The Product Manager, Growth connects product teams with market insights, conducts customer interviews, organizes feedback, and supports go-to-market strategies.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.