Top Machine Learning Jobs in San Francisco Bay Area, CA

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

The role involves designing, building, and deploying computer vision models for vehicle damage detection, covering the entire ML lifecycle and collaborating across teams.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

As an AI Engineer, you will develop and optimize AI systems for legal tasks, work on integrating models, and establish evaluation frameworks to enhance decision-making capabilities in complex legal workflows.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

In this role, you will develop AI foundation models, perform applied research, collaborate with cross-functional teams, and leverage advanced technologies to enhance customer experiences in banking.

Research, design, develop, and test machine learning and robotic applications for autonomous vehicles across the full ML lifecycle. Create novel deep learning models (CNNs, generative AI), improve perception (kinematics, intent, trajectory), integrate multi-sensor data (camera, RADAR, LiDAR) with HD maps, develop auto-labeling and large-scale data pipelines, optimize models, and debug real-time perception issues in a ride-hail fleet.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

Lead vision and roadmap for large-scale recommender systems across Spotlight, Discover, and Friend Stories. Technically lead engineering teams, design next‑generation recommender and retrieval architectures, incorporate SOTA research and foundation models, advance ML infrastructure for scalability and reliability, and partner cross-functionally to drive experimentation, operational excellence, and cost-effective production deployments.

Lead design, development, and operationalization of enterprise-scale AI/ML systems across hybrid environments. Drive platform architecture, model lifecycle management, migration to cloud-native platforms, generative and agentic AI integrations, automation and governance, cross-functional collaboration, and mentorship to accelerate predictive and generative AI adoption.

Lead design and delivery of scalable, secure GCP data and ML pipelines. Build feature stores, standardized transformation libraries (Python/SQL/Beam), orchestration (Composer/Airflow), CI/CD, model training/validation/monitoring, enforce data quality/lineage/observability, optimize cost/performance, and enable self-service for data scientists with documentation and runbooks.

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 Principal AI/ML Engineer will develop advanced ML models for the AdTech platform, lead system design, and collaborate across teams to optimize advertising strategies using AI.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.