Top Machine Learning Jobs in San Francisco Bay Area, CA

The Senior AI Product Manager will lead product initiatives, focusing on AI integration, collaboration with teams to improve user experience, and driving product execution.

As a Research Engineer, you'll work on pretraining, scaling, post-training, and deployment of AI models, improving infrastructure and performance.

As a Senior ML Engineer, you'll design, develop, and deploy ML models for product features, collaborate with teams, and drive research initiatives.

Manage cross-functional AI-related projects, coordinate research efforts, synthesize information for leadership, and identify new initiatives while leading special projects.

The Research Scientist will design methods and experiments for AI control and monitoring, focusing on safety and robustness in advanced AI systems. Key tasks include developing monitoring techniques, researching layered control mechanisms, and collaborating with policymakers and industry experts.

Lead a team to build ML algorithms for Lyft Ads, improving ad relevance and targeting while collaborating with cross-functional teams to drive strategy and execution.

The role involves building and scaling machine learning models to drive marketing performance at SoFi, focusing on customer acquisition, retention, and LTV using data analysis and modeling.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Develop and execute ML technology stack vision for Ads, improve ML models, and enhance content understanding using LLMs in a collaborative environment.

Develop autonomous robot policies, work on hardware-software integration, create data collection methods, and refine machine learning models for robotics.

As a Machine Learning Engineer, you'll own the design, development, and operation of production-grade ML systems in clinical workflows, ensuring model accuracy, reliability, and integration into patient-facing applications.

The Research Scientist will design and develop innovative machine learning models to improve clinical intelligence, driving research from concept to validated results while collaborating with clinicians and engineers.

The Research Engineer will build training infrastructure and reward pipelines, fine-tune models, bridge RL with LLMs, run fast experiments, communicate findings, and collaborate with the research team at Firecrawl.

The Staff Data Scientist will lead advanced analytics and machine learning initiatives, collaborate with Product Management teams, and generate actionable insights on customer behavior to inform product strategies.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

The Principal Machine Learning Scientist II will focus on advancing deep learning methods for biologics discovery, emphasizing protein and antibody design while collaborating with scientists to improve drug discovery efficacy.

As an AI Research Engineer, you'll conduct research in AI and automated reasoning, creating innovative solutions, testing systems, and contributing to scientific publications while collaborating with engineering teams.

Design and deploy machine learning models for content relevance on Patreon. Collaborate with cross-functional teams, analyze data, and iterate on models for performance improvement.

As an AI Researcher, you will develop novel architectures and optimizations in machine learning, collaborating with teams to enhance open models and ensuring cutting-edge research practices.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

Lead and mentor a team of Solution Architects, ensuring technical solutions align with customer needs, while managing deployments and proofs of concept for AI applications.

Lead the design of Data and AI solutions, partner with stakeholders, ensure scalable architectures, and adhere to regulatory standards within Gilead's PDM and Tech Ops.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.