Machine Learning Jobs in Berkeley, CA

The ML Engineer will design and automate evaluation pipelines, curate datasets, deploy voice models, and ensure model performance while scaling workloads.

The Machine Learning Engineer will develop machine learning models, build data pipelines, and collaborate with teams to identify customer needs. Responsibilities include training and validating deep learning models and presenting performance results.

The role involves designing and maintaining ML infrastructure, solving complex technical problems, and collaborating with engineers and data scientists.

Join Roche's AI for Drug Discovery group to build and maintain scalable ML infrastructure on AWS, optimizing CI/CD pipelines and collaborating with ML engineers.

The role involves building and maintaining ML infrastructure, managing data for model training, and collaborating on system performance and deployment.

As a Machine Learning Engineer, you will build AI applications for the food industry, focusing on ML methodologies, solution development, and team collaboration.

As a Senior Machine Learning Engineer, you'll own the ML lifecycle, build scalable data pipelines, and collaborate cross-functionally to enhance Cognitiv's deep learning infrastructure.

The Staff Machine Learning Engineer will design, develop, and optimize machine learning applications, mentor junior engineers, and conduct research to enhance Quantcast's ML systems.

The Staff Scientist will leverage advanced data science and ML techniques to support drug discovery, focusing on model development and collaboration with scientists.

As a Machine Learning Engineer Intern, develop and deploy deep learning solutions, enhance algorithms for real-time bidding, and collaborate across teams. Focus on advanced architectures and algorithms for advertising precision.

Drive the development and implementation of machine learning applications and pipelines, collaborating with teams to enhance and deploy AI models.

The Senior Software Engineer will develop and implement machine learning applications, collaborate with data teams, and uphold coding standards in Python.

Drive AI strategy and lead research for high-growth startups by developing scalable machine learning and deep learning models, optimizing AI systems, and collaborating with engineering and product teams.

Looking for experienced Staff AI and Machine Learning engineers to join early-stage startups, focusing on developing and deploying ML models and aligning AI strategies with product roadmaps.

Develop and implement ML models for search and recommendations, focusing on generative AI technologies and multimodal processing to improve creative experiences.

As a Senior Machine Learning Engineer, you'll design and build production systems for deploying 3D and 4D generative AI models, focusing on usability and scalability.

The ML Research Engineer will develop and train embedding models for a search engine, focusing on novel transformer architectures and dataset creation.

Design and optimize machine learning pipelines for large-scale systems, integrating models into user features and ensuring data integrity and performance.

Lead the design and optimization of ML systems at Scribd, managing cross-team projects, mentoring engineers, and shaping technical direction for ML initiatives.

Design and optimize machine learning systems, build scalable data pipelines, and run experiments to improve recommendations for Scribd's products.

The ML Infrastructure Engineer will develop and scale AI safety systems infrastructure, optimize machine learning pipelines, and ensure reliable system performance while collaborating with research teams.

Lead and develop a team to manage ML infrastructure for AI safety, ensuring reliability and scalability for real-time safety evaluations and production systems.

Develop tools and services for the Anyscale platform, focusing on ML Ops, user interfaces, and critical infrastructure for scalable applications.

The role involves designing and developing algorithms and ML models for machine perception, collaborating with cross-functional teams, and applying research in computer vision to improve systems.

As an ML Infrastructure Engineer, you will build infrastructure for AI development, automate ML workflows, and enhance developer experiences while collaborating with various engineering teams.