Top Machine Learning Jobs in San Francisco Bay Area

Develop and implement machine learning models for fraud detection, analyze large datasets, collaborate with cross-functional teams, monitor and evaluate model performance, and stay up-to-date with the latest machine learning and fraud detection techniques.

Designs, develops, and programs methods and systems to analyze big data sources and generate insights and solutions. Interacts with teams to identify data analysis questions and issues. Cleanses and integrates large datasets from multiple sources. Identifies meaningful insights and communicates findings to managers.

Lead the development and implementation of analytical capabilities and applications, create machine learning models and pipelines, oversee technology proof of concept projects, collaborate with Data Scientists and Analysts, mentor junior engineers, and analyze data to gain insights into user behavior.

Research, design, and build high-impact end-to-end machine learning systems for customer growth. Partner with cross-functional teammates in Product, Design, Content, and Marketing to understand their problems and design solutions. Lead the design of scalable ML solutions in production. Promote and role-model best practices of data science, engineering, and communication throughout the organization. Evangelize ML adoption and innovation across the product and engineering teams. Foster a diverse community of data scientists and ML engineers.

Machine Learning Engineer specializing in NLP to join the Responsible AI team at Grammarly. Develop and implement new ML solutions to improve safety and fairness of Grammarly products.

Design, build, evaluate, deploy and iterate on large scale Machine Learning systems. Understand and optimize the user experience at all stages of the user journey. Work with Product, Design, Infra, and Frontend teams to bring models and features to life. Evaluate the performance of machine learning systems against business objectives.

As a Machine Learning Engineer at Liftoff, you will own ML models that have a direct business impact, adopt new technologies for training and serving ML models, mentor team members, monitor latest ML research, and influence team roadmaps. Requirements include 8+ years of ML experience, strong engineering skills, and a degree in Machine Learning, Math, Physics or similar.