Machine Learning Jobs in Berkeley, CA

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

The ML Platform/MLOps Engineer will build and maintain infrastructure for large-scale ML systems, ensuring efficiency, reliability, and security while supporting the research team from experiments to production models.

Develop Cloud Data Infrastructure for Assured AI in autonomous vehicles, focusing on ML model training, evaluation, and deployment.

Develop deep learning models for autonomy applications, implement ML algorithms for self-driving vehicles, and resolve real-world testing issues.

The Senior ML Performance Engineer will architect a Performance Testing Platform, optimize ML performance on GPUs, and validate model efficiency.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

The role involves developing ML systems for enterprise workflows, focusing on user understanding, task prediction, and optimizing ML pipelines. Key responsibilities include designing scalable evaluation frameworks and collaborating with teams to deliver production-ready solutions.

Contribute to developing machine learning models for multi-scale biology, focusing on design, deployment and optimization across teams and technologies.