Top Remote Senior Machine Learning Jobs in San Francisco Bay Area, CA

Lead development of machine learning models for real-time content ranking, translating business objectives into effective ML solutions while managing experiments and data pipelines.

Lead the development of ML solutions to enhance user experience at Babylist. Build recommender systems and personalization features from scratch, collaborating with a cross-functional team.

The Staff Machine Learning Engineer will define technical strategies, design ML models for document processing and risk assessment, and build scalable ML systems to protect Coinbase users' funds.

The Machine Learning Engineer will scale core infrastructure, manage data pipelines and model evaluation, and improve benchmarking systems. Collaborate with teams to enhance AI evaluation methodologies.

Lead a team to enhance ML-driven trading strategies, conduct market research, build production-quality code, and optimize strategy implementation in a quantitative trading environment.

As a Machine Learning Engineer, you'll design and implement predictive analytics systems, optimize modeling processes, and maintain production systems while collaborating with teams.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Design and validate machine learning models for financial crime compliance, improve data pipelines, and analyze large datasets to mitigate risks in the crypto and fiat ecosystems.

As a Senior Machine Learning Engineer, you'll design and build AI systems, manage ML lifecycles, and create recommendation and retrieval systems while collaborating cross-functionally.

The Solutions Architect for AI/ML at Snowflake will advise clients on best practices for Data Science workloads, design solutions, and build ML pipelines. Responsibilities also include working hands-on with SQL and Python, maintaining technical expertise and collaborating with teams to improve products.