Top Machine Learning Jobs in San Diego, CA

The role involves cultivating technical relationships with Accenture, supporting solution development, customer deal support, and representing Databricks at events.

As the Director of Product Marketing for Databricks AI, you will lead the marketing strategy for AI products, collaborate with teams to create effective campaigns and product launches, and increase global outreach through partnerships.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Lead development and maintenance of internal Python SDK frameworks and agentic workflows, build scalable FastAPI-based ML APIs, automate CI/CD with Jenkins and GitLab Runners, use Galileo for model evaluation and observability, and collaborate across teams to deliver production-grade autonomous systems.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

The AI & Machine Learning Director will lead client engagements, analyze corporate performance, and drive improvements in profitability through machine learning and data analysis, while also managing business development and team collaboration.

The Senior Vice President will lead projects focused on corporate performance, drive revenue growth, and develop business strategies while utilizing data modeling and machine learning techniques for analysis and solutions.

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 Senior Machine Learning Engineer will deploy and maintain ML models, collaborate with cross-functional teams, and optimize ML system performance in a fully remote role.

Design, own, and scale end-to-end ML training and inference pipelines for on- and off-vehicle systems. Build extensible software across vehicle platforms, evaluate new models, grow training data scale, collaborate with ML and robotics engineers, and drive an E2E-first approach for deployment and field operations.

The role involves designing and prototyping advanced AI reasoning systems for real estate, focusing on LLM-powered applications while mentoring junior scientists.

The Director will lead Macmillan's corporate social media and community growth strategy, enhancing engagement and driving measurable growth across digital platforms through strategic content and analytics.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

As a Staff AI Engineer, you will drive product-centric development of AI technologies, enhance evaluation systems, optimize reasoning strategies, and lead production-grade model tuning while balancing performance and cost.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

The Technical Product Marketing Manager will drive product marketing strategy, understand Socure's technology, craft technical narratives, and enhance internal and external communication through enablement materials.

As VP of Engineering, you'll lead the application engineering strategy and team, integrating AI capabilities into products while ensuring quality and execution across backend, mobile, and desktop platforms.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Principal Machine Learning Engineer will design and scale ML systems for personalization and search, mentor engineers, and lead major ML initiatives at Fetch.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.