Top Remote Machine Learning Jobs in Fayetteville, AR

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

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.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Lead the strategy and execution of product marketing efforts, create compelling narratives for products, and collaborate with cross-functional teams to enhance user adoption and achieve growth objectives.

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.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

As a CBRN expert at Alice.io, you will evaluate and create prompts for Generative AI systems, ensuring their safety and accuracy in CBRN contexts. You'll provide expert feedback and stay updated with relevant research and regulations.

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.