Top Remote Machine Learning Engineer Jobs

Design and develop scalable AI/ML solutions while mentoring teams and collaborating with cross-functional partners to enhance customer experience through data-driven insights.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

As a Machine Learning Engineer at Cisco ThousandEyes, you will develop, evaluate, and implement AI/ML models, including Deep Learning and Large Language Models, and manage large-scale ML pipelines.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

The Sr AI/ML Engineer designs and deploys generative AI and NLP solutions, develops large language models, and implements AI systems for healthcare impact.

Design, build, deploy, and maintain AI/ML solutions (including LLMs and Generative AI) for provider-facing products. Lead conversational AI/chatbot initiatives, integrate AI into cloud-based services, ensure responsible and compliant AI use, and collaborate with product and engineering teams to deliver scalable, production-quality solutions.

The role involves leading the design and implementation of AI/ML Voice Bot solutions, managing cloud deployments, and mentoring a team.

Lead design, development, deployment, and maintenance of AI/ML solutions (including LLMs and Generative AI) for healthcare. Drive adoption of responsible AI, build conversational/chatbot systems, integrate AI into cloud services, ensure production reliability, mentor engineers, and align solutions with enterprise AI strategy and product goals.

Lead design, development, deployment, and operationalization of responsible AI/ML solutions (including LLMs, Generative AI, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, ensure security/compliance, integrate AI into cloud platforms, and drive alignment with enterprise AI strategy.

As a Staff Machine Learning Engineer, you will design and implement infrastructure for AI workloads, collaborate on GPU cluster performance, and write scalable code while mentoring colleagues.

Design, develop, test, and deploy generative AI/ML models (LLMs, RAG, agentic AI) into production using agent frameworks, containerization, CI/CD, and cloud platforms; analyze large datasets and optimize system performance.

Lead design, development, deployment, and operationalization of AI/ML solutions (including LLMs and Generative AI) for healthcare. Provide technical leadership on chatbots, conversational AI, and NLP, ensure responsible AI and production reliability, and mentor engineering teams.

Lead end-to-end AI/ML projects: research, prototype, optimize, and productionize ML and LLM solutions for healthcare. Mentor and recruit teams, run experiments and A/B tests, present results to leadership, and pursue AI research and IP.

Lead AI/ML Engineer to design, develop, and deploy AI-powered solutions across the full ML lifecycle. Own architecture, MLOps, monitoring, and scalability. Partner with product, data science, and platform teams, mentor junior engineers, and drive adoption of best practices for production ML and generative AI solutions.

Lead design, development, deployment, and maintenance of AI-powered solutions (LLMs, Generative AI, traditional ML) for provider-facing products. Build chatbots and conversational NLP, integrate AI into cloud (preferably AWS), ensure responsible AI practices and compliance, provide technical leadership, mentor engineers, and collaborate with product and architecture teams.

Lead development of AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, ensure responsible AI compliance, produce documentation/patents/publications, and collaborate on MLOps pipelines and stakeholder integrations.

Design, develop, and deploy production-grade AI/ML models and pipelines (LLMs, RAG, Transformers). Work with agentic frameworks, scale training/inference on Databricks/PySpark, integrate solutions via Docker and REST APIs, leverage cloud services (Azure/AWS), participate in AI governance, and mentor junior engineers to deliver measurable pharmacy optimization outcomes.

Lead end-to-end generative AI and ML initiatives: design scalable AI architectures, build and productionize models, run experiments and A/B tests, analyze healthcare datasets, implement MLOps, research novel approaches, and mentor/hire AI/ML engineers.

Lead adoption and delivery of AI/ML solutions (including LLMs, generative AI, chatbots, and predictive analytics). Design, build, deploy, and maintain cloud-hosted AI services, ensure responsible AI and compliance, provide technical leadership, mentor team members, and integrate AI into product and platform initiatives.

The role involves building computer vision models, researching deep learning methods, handling large datasets, and implementing MLOps best practices.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

The Senior AI ML Engineer will develop AI systems, ensure model quality, lead architecture governance, and manage observability for healthcare solutions.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.