Top Machine Learning Jobs in Baltimore, MD

Develop, train, and optimize ML models using Python and PyTorch; deploy and maintain ML models in production; collaborate with teams to turn business problems into ML solutions.

As a Senior Machine Learning Engineer, you'll design and implement ML models, develop data pipelines, and collaborate on AI features at Mural.

The ML Platform Engineer will develop and scale ML infrastructure, automate workflows, and collaborate with data scientists to deploy models efficiently in production.

The role focuses on enhancing detection frameworks using machine learning to identify malicious activities and minimize risks for Netflix.

Build and deploy ML systems that detect fraud and automate security decisions, while collaborating with cross-functional teams.

Develop and optimize machine learning models for financial and operational workflows at Netflix, ensuring model reliability and integration with studio applications.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The Machine Learning Engineer will fine-tune and deploy Large Language Models, optimize ML models, maintain data pipelines, and collaborate on AI features.

The role involves building and maintaining ML pipelines, optimizing workflows, and contributing to product strategy with a focus on usability.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.

Responsible for developing production-grade machine learning systems, managing end-to-end ML pipelines, and collaborating on integration with applications.

The Principal Machine Learning Engineer will build and maintain end-to-end ML systems, fine-tune models, and manage production deployments, ensuring robustness and efficiency.

This role leads the research direction for AI, focusing on evolving machine learning systems, aligning product concerns, and ensuring rigorous evaluation of systems.

As a Software Engineer, AI/ML GenAI, you will design and implement AI features, manage embeddings, and optimize AI systems for production deployment while collaborating with product and design teams.

The Sr. Machine Learning Engineer will build tooling and services for ML and generative AI solutions, focusing on deployment, evaluation metrics, and cross-functional collaboration.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

The role involves designing and implementing data and AI architectures, developing data pipelines, and integrating machine learning solutions while fostering Agile methodologies and collaboration.

The Senior Product Manager will lead the strategy for an AI assistant, ensuring compliance and user satisfaction while coordinating with cross-functional teams throughout product development.

The Senior Machine Learning Engineer will build and operate ML systems, manage the model lifecycle, and mentor team members while ensuring sustainability data accuracy.

The Machine Learning Engineer will work on sensing and perception algorithms, deploying computer vision algorithms, and collaborating for code testing and evaluation.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

The role involves developing ML algorithms for autonomous driving, including training models, evaluating performance, and improving behavioral models.

As an AI/ML Engineer, you will design, train, and deploy machine learning models, optimize model performance, and collaborate with cross-functional teams to integrate AI capabilities into products.

As a Machine Learning Engineer, you'll architect data pipelines, build ML training workflows, integrate event streams, and collaborate with teams to improve model performance and ensure compliance.