Top Machine Learning Engineer Jobs in San Francisco, CA

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.

Lead development and production deployment of advanced ML and time-series models for water and infrastructure systems, perform research in Physical AI, collaborate cross-functionally, communicate impact to stakeholders, and mentor junior data scientists.

As a Senior Machine Learning Engineer, you will design and build ML models for patient-provider matching, focusing on personalization and ranking. Responsibilities include model development, A/B testing, and collaborating across teams to improve patient experiences.

The intern will work on projects involving revenue optimization and risk mitigation for solar and storage projects, utilizing machine learning and optimization techniques.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

The role involves designing ML systems for Search Ads, focusing on improving ad relevance and personalization with large language models and deep learning.

Lead the development of machine learning models for ad bidding at Faire, optimizing performance and influencing marketplace strategy.

Lead the Applied Machine Learning team at ExtraHop to develop systems for behavioral detection in network security. Manage model design, evaluation, and operationalization while maintaining experimental rigor and collaborating with product teams.

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

Intern will work on ML model experimentation and integration for improving perception systems, focusing on real-world application and collaboration.

As a Machine Learning Engineer, you will design large-scale datasets, evaluate speech models, implement data generation techniques, and ensure model performance across multiple languages.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

As an AI/ML Engineer Intern at Planet, you will develop advanced machine-learning methods for geospatial analysis and image processing. You will work on designing and implementing algorithms while collaborating with experienced engineers and researchers to translate prototypes into scalable software solutions.

The Director of Machine Learning Engineering is responsible for leading AI strategy, overseeing ML solutions, driving innovation, building teams, and ensuring operational excellence in ML systems.

As a Software Engineering Intern, you'll develop features for user-facing products, work on API platforms, and design data pipelines while gaining hands-on experience in AI technologies.

The Senior Software Engineer will lead projects and develop AI/ML features using Python and Go, focusing on model services and APIs.

The Staff Machine Learning Engineer will architect and optimize data infrastructure for multimodal AI models in oncology, focusing on data management and processing pipelines.

The Senior Lead Machine Learning Engineer will design and implement ML applications, optimize systems, collaborate with teams, and ensure model governance.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

As a Machine Learning Engineer, you will own the execution layer for ML projects, including data preprocessing, training, benchmarking, and collaboration with researchers to deliver actionable results.

Develop and deploy state-of-the-art physical AI for outdoor robotics, focusing on perception, planning, control, simulation-driven training, multi-sensor integration, data pipelines, and GPU-optimized training and inference.

Design, implement, and deploy machine learning algorithms and scalable ML solutions; collaborate with data scientists, engineers, and federal customers to build analytics capabilities and features; support application deployment and customer enablement.

Develop and implement advanced reinforcement learning methods and distributed systems for AI agents. Collaborate with customers to understand needs and create tailored solutions.

Develop machine learning models for search ranking and personalization at Faire, leveraging data to help local retailers thrive.