Top Machine Learning Engineer Jobs in San Francisco, CA

Build and deploy machine learning models for identity, credit, fraud, and other risk domains. Lead research, model development, deployment, monitoring, and maintenance while partnering with Engineering, Product, Design, and Operations. Develop scalable modeling frameworks and integrate LLMs to improve analysis and insights. Identify data-driven product opportunities, solve cross-functional risk problems, and communicate findings to technical and non-technical stakeholders.

Build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML applications, distributed data pipelines, cloud-based infrastructure, model monitoring, retraining, CI/CD automation, testing, governance, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while optimizing algorithms and supporting large-scale containerized systems.

Leads applied science strategy for AI-powered products, designing LLM evaluation frameworks, offline experiments, quality standards, and production monitoring. Builds and evaluates agentic, retrieval-augmented, and multimodal AI systems while partnering with engineering and product leaders. Translates customer and business goals into measurable outcomes, drives experimentation and launch decisions, promotes reliability and MLOps practices, mentors technical staff, and communicates findings and recommendations to diverse stakeholders.

Lead end-to-end development and deployment of deep learning and classical machine learning models for cash flow underwriting. Own model framing, training, evaluation, inference, monitoring, retraining, and production performance. Design ML infrastructure, partner with Product, Engineering, Analytics, and Risk, establish technical direction, mentor teammates, and ensure model governance, explainability, fairness, and measurable member and business outcomes.

Twelve-week Machine Learning Engineer internship developing, deploying, and operating production-scale ML models and pipelines. The intern will lead a research-to-production project, apply supervised, unsupervised, or reinforcement learning techniques, collaborate with engineering and product teams on blockchain and crypto use cases, and present findings to stakeholders.

Design, build, validate, and deploy machine learning models; perform EDA and statistical analysis; design and maintain scalable ETL/ELT data pipelines; work with large distributed datasets; run A/B tests; monitor production model performance; partner with product, engineering, and business teams to deliver data-driven solutions and ensure data quality.

The Model Behavior Engineer at Notion will ensure high-quality AI experiences through context engineering, data analysis, evaluative strategies, and collaboration with AI research labs. Responsibilities include designing tests, debugging, measuring quality, and developing systems to operationalize AI performance. This role blends product thinking with analytical skills to drive enhancements for AI tools.

Build and deploy production machine learning systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, lead-time, spend, anomaly detection, and supplier-risk modeling; creating data pipelines and real-time inference systems; establishing MLOps, governance, and modeling standards; defining the AI transformation roadmap; partnering with business and technical teams; and mentoring junior scientists.

Leads hands-on development of AI and machine learning systems for patient experiences, including matching, ranking, recommendations, onboarding, and personalization. Builds production models, shared ML infrastructure, training and inference pipelines, evaluation frameworks, and operational tooling. Partners with ML and Patient Engineering teams on architecture, experimentation, model reliability, and technical standards. Guides decisions between custom ML, foundation models, deterministic software, and hybrid solutions while mentoring engineers and driving measurable product impact.

Join the IT AI Automation team to build and harden micro-apps and AI integrations on a governed GCP platform. Contribute to bug fixes, small features, code and design reviews, monitoring, and apply foundational software development and security practices. Gain hands-on experience with Python, SQL, CI/CD, and AI tools while supporting deployments, secrets management, testing, and documentation.