Top Machine Learning Jobs in San Francisco Bay Area, CA

The Data Scientist will build AI-driven fraud and risk systems, work with real-world data, and leverage graph techniques to improve detection and labeling. They will partner with engineering to scale data systems in the compliance domain.

Lead and build an in-house data annotation team, define processes and tooling, coordinate with ML and engineering, create documentation, manage annotators, and perform annotations when needed.

Lead and manage a team researching embedding models and retrieval systems for AI, focusing on relevance, adaptive reasoning, and innovative representation techniques.

As a Research Scientist, develop machine learning techniques, advance research agendas, and collaborate with peers to achieve impactful results.

The Director of Global Alliances AI manages partnerships, ensuring strategic alignment and providing insights to optimize collaboration. Responsibilities include overseeing governance forums, engaging senior leadership, and supporting business development efforts for AI-enabled initiatives.

Lead product strategy for Suno's research org by defining evaluation frameworks, connecting creator needs to model capabilities, prioritizing research, running experiments, and bringing model improvements to market in partnership with researchers and cross-functional teams.

Join our AI lab focusing on developing software agents like Devin and Windsurf, fostering collaboration in AI for engineering tasks.

The role involves designing and training deep learning models for genomic analysis, collaborating with scientists, and publishing research findings.

As a Technical Program Manager for Research, you will define and build programs for research teams, identify opportunities for impact, drive execution of research initiatives, and enhance processes in AI development.

Lead a team of engineers in developing cutting-edge perception systems, overseeing technical roadmaps and collaborating across disciplines to enhance AI capabilities in vehicle technology.

Design and develop large-scale HD mapping datasets, data pipelines, and benchmarking tools. Collaborate with ML and cross-functional teams to curate data, improve model training strategies, and scale mapping and update workflows for autonomous vehicles.

Lead high-impact Perception teams, oversee technical roadmap and milestone goals, implement AI architectures, and advance perception capabilities using multi-modal sensor data.

Lead big-data analytics projects: acquire and consolidate diverse data, build and validate predictive and statistical models, analyze complex trends, present actionable insights, collaborate with partners, and ensure compliance of data practices.

Conduct research on AI tokenomics by evaluating model architecture trends, building simulation models for training and inference efficiency across hardware and workloads, diligencing companies using model tokens, and forecasting economic impacts for investors, corporates, and policymakers.

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.

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.

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.

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.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

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.

Customer-facing ML engineer who develops, validates, and deploys 2D/3D computer-vision models on CT data for defect detection, dimensional analysis, anomaly detection, and process control. Responsibilities include scoping projects with customers, end-to-end model lifecycle (training, deployment, monitoring, retraining), collaborating with product and engineering to productize solutions, and producing technical documentation. ~30% travel to customer sites.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

As a Manager in Data Science, you'll lead the development of machine learning models, partner with engineering and product teams, and analyze large datasets to enhance customer experiences and prevent fraud.