Top Machine Learning Jobs in San Diego, CA

As a Machine Learning Engineer, you'll build scalable ML systems for personalized recommendations, collaborating with data scientists and cross-functional partners.

The Senior Machine Learning Engineer will develop ML platforms, optimize model lifecycles, lead initiatives, and maintain code quality, collaborating with Data Science teams.

Lead the development of advanced recommendation systems and ML pipelines. Collaborate with cross-functional teams to enhance user engagement through optimization of machine learning models at scale.

Lead the design and development of AI solutions, optimize algorithms, collaborate with teams, and develop advanced models to drive business insights.

The role involves developing and optimizing intelligent agents using large language models, collaborating with the team, and solving technical problems.

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

The engineer will benchmark and optimize ML models for performance, focusing on throughput and memory within hardware constraints.

The Principal Machine Learning Engineer will develop multimodal technologies, mentor engineers, and drive product innovation in agentic AI systems.

Lead technical strategy and architecture for AI projects, manage client engagements, mentor engineers, and drive innovation in machine learning solutions.

The Junior Machine Learning Engineer will contribute to various AI and ML projects, work on real-world applications, and receive mentorship.

Design and maintain production-grade ML systems, develop generative AI models, architect data pipelines, and collaborate with teams to solve business problems.

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.

Responsible for deploying and improving MLE solutions, building production data pipelines, and writing scalable machine learning systems while collaborating with teams to drive strategy and communicate results.

As a ML Ops Engineer, you will optimize model performance, monitor inference costs, and implement scalable solutions for AI applications.

The Lead Data/ML Engineer will design and maintain scalable data pipelines and AI systems, ensuring data reliability and collaboration with product teams.

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 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 Applied Scientist Intern will develop and deploy AI models, build ETL pipelines, and collaborate to enhance AI quality in legal tech.

This role involves designing, developing, and scaling machine learning systems for payment flows and recommendations, leading technical initiatives, and optimizing complex financial decision systems.

The Staff Machine Learning Engineer will design, develop, and deploy scalable ML algorithms, lead experimentation, and collaborate across teams to enhance AI solutions.

Lead the design and implementation of machine learning platforms at Yahoo, focusing on architecture, governance, and system performance while mentoring teams and influencing industry trends.

Develop and implement machine learning models, analyze data, collaborate on solutions, and optimize systems for scalability while staying updated with AI advancements.

Lead ML research and development, collaborating with engineers and clinicians to create enterprise-grade products and establish a research agenda while sharing insights through publications and presentations.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.