Top Remote Machine Learning Jobs in San Diego, CA

Lead the development of AI agents and infrastructure, collaborate with stakeholders, design APIs and SDKs, and advocate for best practices.

The Practice Manager leads Data Science, AI, and ML projects, managing teams, fostering growth, and ensuring customer success through technical guidance and strategic business planning.

Design and deploy machine learning models for autonomous vehicles, focusing on data tagging, dataset curation, and model validation.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

The Solutions Architect for AI/ML at Snowflake will advise clients on best practices for Data Science workloads, design solutions, and build ML pipelines. Responsibilities also include working hands-on with SQL and Python, maintaining technical expertise and collaborating with teams to improve products.

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

The role involves developing AI-driven features for observability tools, collaborating with cross-functional teams, and delivering scalable solutions.

The role involves designing, building, and managing data pipelines and machine learning solutions for veterinary care, utilizing cloud services and various programming languages while collaborating within an Agile team.

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

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 Senior Machine Learning Engineer will lead AI initiatives, develop ML infrastructure, establish data engineering best practices, and collaborate across teams to enhance healthcare technology services.

As a Machine Learning Engineer, you will fine-tune models, shape data pipelines, evaluate AI behavior, and collaborate on product features in a high-autonomy environment.

The role involves fine-tuning machine learning models, designing datasets and pipelines, and ensuring product quality and safety for a new AI application.

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

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.

The Staff AI/ML Engineer will lead AI product development, ensure system health, and collaborate with teams on product features while maintaining security and compliance.

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

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

Design and deploy machine learning models for personalized recommendations and search on Zillow's platforms. Collaborate with cross-functional teams to enhance user experience.

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

The Principal Machine Learning Engineer leads the design and implementation of advanced ML systems to enhance cybersecurity capabilities, collaborating with Data Scientists and Engineers.

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