Top AI & Machine Learning Jobs in San Francisco, CA

Lead and grow an engineering team building Jade, Chime's AI-powered assistant. Drive technical direction for LLM-powered member-facing features, ensure reliability, safety, and scalability, collaborate with product/design/data/risk, maintain on-call and observability practices, and coach engineers on delivery and career growth.

Leads paid marketing analytics strategy, including attribution, experimentation, incrementality, marketing mix modeling, offer measurement, and budget allocation. Partners with Marketing, Finance, Data Engineering, and Data Science teams to turn analysis into executive decisions. Defines measurement frameworks, improves data models and pipelines, uses AI tools to enhance analytics, forecasts marketing impact, and mentors senior data scientists and contractors.

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.

Leads credit risk strategy and data science for unsecured lending products. Develops underwriting policies, credit models, pricing, line management, repayment, and collections strategies using bureau, alternative, and internal data. Designs experiments, forecasts losses and unit economics, builds monitoring dashboards, and partners with product, engineering, compliance, legal, fraud, and finance teams to deploy and govern risk decisions. Mentors other credit risk data scientists and communicates analytical trade-offs to executives.

Lead a team of data scientists and machine learning engineers building production models for customer acquisition, engagement, retention, segmentation, lifetime value, churn, attribution, referrals, and personalized communications. Set ML strategy for growth and marketing, establish development and deployment best practices, use behavioral and transactional data to optimize customer outcomes, and collaborate with marketing, product, growth, and engineering stakeholders.

Build and evolve internal AI platforms, APIs, SDKs, guardrails, and shared services that enable safe, observable, cost-efficient LLM applications. Prototype and productionize AI capabilities, establish evaluation and governance practices, and standardize development patterns across the company. Partner with Platform Engineering, Security, Risk, Legal, Product, and Operations while participating in an on-call rotation.

Designs and operates scalable machine learning and AI infrastructure on AWS, supporting training, inference, feature pipelines, LLM and agentic workloads, evaluation, observability, governance, and developer tooling. Builds distributed processing, streaming, infrastructure-as-code, and CI/CD systems while partnering with data science and ML engineering teams. Requires production experience with distributed systems, ML platforms, LLM application patterns, cloud infrastructure, containers, orchestration, and software engineering fundamentals.

Partner with Growth Product and Engineering to own funnels end-to-end, design and analyze A/B tests, build metrics and dbt data pipelines, apply causal inference and ML, enable self-service analytics, improve eventing, and translate analyses into executive-ready recommendations to drive acquisition, conversion, and retention.

Posting contains company overview, culture, benefits, and hybrid work policy. No specific responsibilities, technical requirements, or deliverables were listed for the Senior Data Scientist, Membership role.

Design, train, deploy, and monitor large-scale sequential and deep learning models (including transformers) to personalize growth and marketing experiences using financial and behavioral data. Collaborate with Growth, Product, and Engineering to build scalable ML infrastructure, experimentation frameworks, and production MLOps for real-time and batch systems that improve member engagement.