Top AI & Machine Learning Jobs in San Francisco, CA

Build and deploy AI-enabled workflows across internal departments, partnering with business leaders and subject-matter experts. Design agentic solutions, evaluations, integrations, tools, and reusable platform capabilities. Establish operating models for discovery, onboarding, adoption, production support, and continuous improvement. Translate deployment insights into enterprise AI platform investments while advising senior technical and business stakeholders.

The Credit Strategy Lead will analyze data to propose credit risk strategies for lending products, collaborate with teams to drive revenue, and develop innovative risk management approaches.

Leads enterprise-wide AI transformation by developing strategy, identifying high-value generative and agentic AI opportunities, guiding governance and responsible adoption, and delivering measurable business outcomes. Partners with executives and cross-functional technology and business teams, leads product managers and engineers, evaluates AI technologies and vendors, establishes KPIs, and recruits and develops the AI transformation organization.

Develop, validate, and deploy credit underwriting machine learning models. Lead proofs-of-concept for alternative data, collaborate cross-functionally on model deployment, ensure regulatory and fair-lending compliance, and present model insights to credit and business leaders.

Lead and grow a team of data scientists for SoFi’s consumer finance businesses. Prioritize and deliver high-impact data science and ML projects, build data pipelines, deploy and monitor models in production, mentor engineers, recruit talent, and collaborate cross-functionally with product, engineering, finance, risk, and executives to drive data-informed business decisions.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.