- Location
- San Francisco - CA; New York City - NY; Salt Lake City - UT · CA - San Francisco
- Department
- Finance
- Seniority
- Entry
- Experience
- 1+ years
- Education
- Master
Description
Employee Applicant Privacy Notice
Who we are:
Shape a brighter financial future with us.
Together with our members, we’re changing the way people think about and interact with personal finance.
We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.
The role:
SoFi's Associate AI Engineer, Finance Transformation is a hands-on builder within SoFi's Finance organization, focused on building agentic AI workflows that transform how Finance works from close and reconciliations to forecasting and reporting.
Finance has one of the largest AI opportunity surfaces at SoFi: over a hundred identified use cases, an active champions network, and executive sponsorship. In this role you will build multi-step AI workflows on approved enterprise AI platforms, stand up the telemetry that measures AI usage, cost, and ROI across Finance, and help make AI outputs trustworthy enough for Finance decision-making in a controlled environment where outputs must be explainable, auditable, and reconciled to the number.
You will work directly with the AI Transformation Manager for Finance, who owns use-case strategy and stakeholder engagement, and in close partnership with SoFi's AI SDLC and platform teams, who support the path from prototype to production. This is a build-focused role with an unusual growth surface: SoFi's AI Engineering ladder (through Staff and Senior Staff) is the visible progression path.
What you’ll do:
- Build agentic AI workflows: Develop multi-step AI workflows such as planning, tool use, retrieval, structured orchestration on approved enterprise AI platforms and agentic coding tools to automate Finance processes.
- Build AI telemetry: Own usage, cost, engagement, and ROI reporting across Finance AI tools, from source extraction to dashboards leadership relies on.
- Contribute to the experience layer: Help design how Finance users interact with AI systems including workflows, interfaces, and feedback loops that build trust and adoption.
- Validate outputs: Apply and refine repeatable patterns for validating AI outputs before they inform Finance decisions such as reconciliation, documented assumptions, human-in-the-loop checkpoints.
- Prototype fast: Turn prioritized Finance use cases into working prototypes, then partner with AI SDLC and platform engineering on the path to production.
- Learn the domain: Develop Finance fluency (close, reconciliation, reporting workflows) and SQL rigor in seat, with structured support.
What you’ll need:
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field, or equivalent practical experience.
- 1–3 years of experience building with LLMs or an exceptional new-grad record with substantial hands-on agent projects (academic, personal, open-source, or internship work all count).
- Demonstrated hands-on experience with LLM APIs, prompt engineering, RAG, or agent frameworks — with a portfolio of projects where you can clearly explain what you personally designed and why.
- Working Python for automation, API integration, and prototyping.
- Basic SQL for querying data (Finance-grade rigor will be developed in seat).
- Builder mindset: bias toward making a working thing rather than describing one.
- Willingness to learn Finance domain context and communicate with non-technical stakeholders, with support.
Nice to have:
- Exposure to evaluation, tracing, or observability practices for AI systems.
- Exposure to Snowflake, Cortex, or natural-language-to-SQL tooling.
- Exposure to Finance, Accounting, or another controlled/regulated data environment.
- Experience building user-facing tools or internal apps powered by AI.
