Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Early Career Analyst supporting Affirm’s Risk & Analytics organization through data analysis, portfolio monitoring, reporting, risk and growth strategy insights, and cross-functional collaboration. The role uses SQL and Python to build scalable reports, support machine learning and risk model deployment, assess lending regulations, and improve product launches and market expansion. Analysts will integrate AI tools into workflows while validating outputs and developing expertise in consumer lending and quantitative business decision-making.

Engineering interns develop, ship, test, and monitor production code through a meaningful project tailored to team needs. They collaborate with mentors and managers, contribute to real engineering systems, and present their work at the end of the internship. Projects may involve data pipelines, automated testing, financial platforms, frontend applications, APIs, deployment, and cloud technologies.

Entry-level software engineers will contribute to team projects, write tested and extensible code, navigate large codebases, debug and review code, collaborate with stakeholders, manage quality and delivery trade-offs, and actively pursue professional growth. The role is part of Affirm’s early-career program and involves hybrid work in New York City or San Francisco during the first year.

Develop and ship a meaningful engineering project, monitor its production deployment, collaborate with a mentor and manager, test and document work, and present results to the engineering organization. The role may involve machine learning, backend or frontend development, APIs, data processing, deployment, and testing using technologies such as Python, Java, JavaScript, AWS, AngularJS, or React.

Software Engineering Interns develop, deploy, test, monitor, and present meaningful production projects during a 12–16 week internship. Interns collaborate with mentors and managers to define projects aligned with team needs, ship code, and verify production performance. Work may involve backend or frontend development, data processing, automated testing, APIs, cloud platforms, and financial technology.

Negotiates and manages complex global commercial agreements for Affirm’s Revenue team. Partners with Sales, Strategic Partnerships, Product, Finance, Risk, Privacy, and other teams to provide practical legal guidance. Builds contract playbooks, templates, negotiation guides, training, and scalable processes that improve efficiency while managing risk. Supports fintech and payments transactions across multiple jurisdictions and advises stakeholders on privacy and data protection requirements.

Evaluate, structure, and price consumer asset-backed transactions; develop capital structuring, pricing, and quantitative loan-performance models; and provide funding strategy insights. Partner with Capital Markets, Treasury, Merchant Pricing, Credit, Commercial, Product, and Engineering teams to support funding, product, and business decisions. The role also involves collaborating with accountants, rating agencies, and internal and external partners.

Supports Affirm’s global licensing and regulatory program by coordinating state and international licensing requirements, annual reports, renewals, NMLS filings, call reports, and regulatory obligations. The role partners with Legal, Compliance, Finance, Tax, Product, and senior leadership, maintains licensing repositories and workflows, identifies issues, develops efficient processes using AI, and assists with regulatory research and licensing initiatives.

Own and deliver quarterly engineering goals for Affirm’s ML Feature Platform. Design, develop, and launch scalable backend systems, lead engineers through ambiguous problems, collaborate with product and design stakeholders, improve platform reliability and availability, support on-call operations, establish quality standards, and mentor team members. The role focuses on highly available distributed systems supporting machine learning feature creation, storage, serving, and offline backfilling.

Own and deliver goals for ML training and serving infrastructure. Lead engineers through ambiguous, distributed-systems challenges; design, develop, and operate highly available backend platforms; monitor reliability and support on-call operations. Collaborate with product and engineering stakeholders, improve code and design standards, identify and resolve technical issues, and mentor team members. The role focuses on scalable systems that enable production model training and serving.