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Design, build, and operate core AI platform components—including the AI Gateway, model routing, evaluation platform, and harness vending—while owning end‑to‑end platform systems. You’ll also instrument AI economics, set technical standards, drive operational excellence, and mentor engineers.
A Bachelor’s (3‑4 yrs) or Master’s (2‑3 yrs) in CS or a related field, with advanced expertise in a modern backend language (Java, Kotlin, Python, Go or Scala) and proven experience building distributed systems. Strong architectural judgment, ability to lead design reviews, and a track record of mentoring are also required.
The AI DevEx team builds the foundation that lets every SoFi engineer ship faster, safer, and with AI‑enhanced productivity, making the platform critical to the company’s transformation. Your work will directly enable the broader engineering organization to adopt interactive coding tools and manage AI spend.
Engineers who thrive on building large‑scale, production‑grade platforms, enjoy setting technical standards, and like mentoring others. Ideal candidates are comfortable with distributed systems, have strong backend coding skills, and want to shape AI tooling used across the company.
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The role:
SoFi's AI Platform team builds the foundation that enables every engineer at SoFi to ship faster, safer, and with higher leverage from AI. Our AI Developer Experience (AI DevEx) team owns SoFi's interactive coding strategy and underlying infrastructure: AI economics, coding harnesses and harness vending, our evaluation platform, the enterprise AI Gateway, and intelligent model routing.
As a founding Staff Software Engineer, you'll be a technical leader on the team: owning major platform systems, setting the team's engineering standards, and working closely with our Autonomous Coding, CI/CD, and Code Factory teams and the engineering teams we serve.
What you’ll do:
Own platform systems end-to-end: design, build, and operate foundational components of the AI Gateway, model routing, eval platform, and harness vending, transforming broadly defined challenges into production-grade systems.
Instrument AI economics: develop usage telemetry, cost allocation, and chargeback mechanisms that provide transparent visibility into AI spend per developer.
Lead team execution: plan feature architecture and implementation paths, parallelize work streams across teammates, and align cross-team dependencies with product and engineering leaders.
Guide technical design: facilitate design reviews, evaluate architectural trade-offs, and align critical technical outcomes with the broader SoFi engineering community.
Establish technical standards: define coding conventions, automated patterns, and architectural standards that result in maintainable, extensible software for other engineers.
Drive operational excellence: automate build, test, and deployment workflows; serve as a leading resolver on production incidents; and resolve root causes through post-incident actions.
Maintain a 6-to-12-month technical vision: anticipate system growth, prioritize architectural hygiene, and make pragmatic build-versus-adopt choices across an evolving tooling ecosystem.
Partner with internal engineering teams: engage directly with developers as primary customers to understand their workflows, drive adoption of interactive coding tools, and translate feedback into continuous system enhancements.
Mentor and develop engineers: provide well-calibrated, constructive code and design feedback to accelerate team member growth.
What you’ll need:
Education & Minimum Experience Requirements: Bachelor’s Degree + 3–4 Years of Experience OR Master’s Degree + 2–3 Years of Experience in software engineering, computer science, or a related field.
Advanced programming expertise in at least one modern backend language (such as Java, Kotlin, Python, Go, or Scala).
Proven distributed systems expertise with sound judgment evaluating trade-offs across security, concurrency, fault tolerance, horizontal scaling, and idempotence.
Track record of technical leadership guiding feature delivery, cross-functional alignment, and architecture on production platforms.
Hands-on experience developing with or building AI-assisted developer tooling and LLM-based systems (e.g., coding assistants, autonomous agents, prompt engineering, and tool execution frameworks).
Hands-on experience with cloud infrastructure (AWS/GCP), containerization, modern CI/CD pipelines, and operational on-call service ownership.
Product-oriented engineering mindset: commitment to treating internal developers as customers and measuring success through platform adoption and user sentiment.
Clear written and verbal communication skills, including the ability to draft structured technical design documents and present options to diverse stakeholders.
AI-Assisted Engineering Expectations:
Apply AI tools responsibly to prototype solutions, navigate unfamiliar codebases, and accelerate implementation velocity while upholding high security and safety standards.
Critically inspect and validate AI-generated code, backing all implementations with comprehensive automated tests to ensure long-term maintainability.
Maintain rigorous compliance with data governance and security policies, ensuring sensitive member data and proprietary assets are never exposed to unapproved tools.
Elevate team productivity by establishing and sharing proven harnesses, prompt templates, and operational guardrails.
Nice to have:
Direct experience building or operating LLM evaluation pipelines, model proxies, or semantic request routing layers.
Experience engineering internal developer platforms (IDP), developer tooling, or CLI workflows.
Experience with cost telemetry, usage-based metering, or FinOps instrumentation.
Background in fintech or adjacent environments operating under strict data compliance and security regulations.
