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

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.

Lead and grow a search backend engineering team responsible for indexing, ranking, query and content understanding, personalization, ML retrieval, and multimodal/LLM-based search innovations. Drive roadmap, collaborate cross-functionally with product, research, infra and data teams, provide leadership visibility, mentor engineers and managers, and ensure scalable production systems impacting hundreds of millions of users.

Lead and grow an 8–9 person machine learning engineering team focused on Homefeed retrieval. Own candidate generators, ML optimization engines, signal integration, and explore generative retrieval approaches. Provide technical direction, cross-functional partnership, stakeholder reporting, and culture development to drive large-scale recommender improvements and product impact.

Lead design and implementation of large-scale recommendation and decisioning systems for advertiser and seller workflows. Build unified context layers and agent foundations, own end-to-end ML lifecycle from problem framing to deployment, develop evaluation and feedback loops, apply retrieval/ranking/embeddings and agentic AI techniques, and mentor engineers while driving multi-quarter ML roadmaps.

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 operationalize data architectures, instrumentation, and analytical frameworks to support SEO and AI-driven discovery. Partner with data engineering and BI to build reliable datasets, monitoring, and automation. Prototype and productionize LLM-based workflows and agent-like systems, translate marketing needs into scalable decision systems, and document assumptions and processes.

Own stability, performance, and reliability of AI-backed backend systems. Translate customer problems into simple solutions, ship production-quality Django/Python services, collaborate with Product and Design, and manage complexity while delivering product value.

Founding full-stack engineer to build and ship production LLM-powered features and core product surfaces (employee UX and manager dashboard). Work across web and desktop stack, own features end-to-end, set up telemetry/eval systems, join pilot calls, convert pilots to customers, and shape engineering culture and architecture as the company scales.

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.

The Staff AI Engineer will design, implement, and maintain machine learning algorithms and systems, integrating AI components into applications and collaborating with cross-functional teams using cloud technologies and DevOps principles.