Senior Staff Software Engineer, AI Developer Experience

SoFi•San Francisco, CA

About The Position

SoFi's AI Platform team builds the foundation that helps engineers ship faster and more safely using AI. The AI Developer Experience (AI DevEx) team owns the company's interactive coding strategy and its supporting infrastructure, including AI economics, coding harnesses and harness vending, an evaluation platform, the AI Gateway, and intelligent model routing. As a founding Senior Staff Software Engineer, you will serve as the technical lead and subject matter expert for AI DevEx across AI Platform. You will set architecture, shape interactive coding strategy, and lead work across Autonomous Coding, CI/CD, Code Factory, and customer engineering teams. The role spans technical direction, platform strategy, distributed-systems architecture, operational excellence, and mentoring.

Requirements

Bachelor’s degree plus 4 or more years of experience, or master’s degree plus 3 or more years of experience, in software engineering, computer science, or a related technical field.
Proven technical leadership on software initiatives spanning multiple teams and complex distributed architectures.
Strong system-design capability with large-scale, high-availability distributed systems, platform infrastructure, and cross-domain APIs.
Hands-on experience building with LLM-based architectures, model APIs, and AI-assisted development tools, such as coding agents, developer harnesses, prompt/context evaluation, and tool integration.
Hands-on experience designing or operating model evaluation pipelines, AI proxies or gateways, or dynamic routing systems.
Ability to analyze infrastructure unit economics, capacity trade-offs, and operational telemetry.
Exceptional communication and cross-functional leadership skills, including a record of driving technical alignment across engineering squads and leadership.
Commitment to engineering best practices, security guardrails, data privacy, and maintainable software design.
Ability to review AI-generated code and architecture critically and enforce rigorous automated testing, safety, and long-term maintainability.
Ability to adhere to data-handling and security protocols protecting proprietary and member data.

Nice To Haves

Experience designing or scaling internal developer platforms (IDPs) and developer tooling at enterprise scale.
Practical exposure to FinOps frameworks for tracking and optimizing cloud or AI compute spend.
Direct experience developing autonomous agentic workflows or deep integrations into engineering development lifecycle tools.
Background in financial technology, banking platforms, or regulated engineering environments.

Responsibilities

Define and drive architecture for the enterprise AI Gateway, model routing, automated evaluation platform, and harness vending, supporting interactive and autonomous workflows across business units.
Identify developer productivity bottlenecks, establish a 12–24 month technical roadmap, and execute high-impact design and build initiatives.
Architect cost models, metering systems, telemetry, and adaptive routing policies that balance model accuracy, latency, and operational spend.
Plan and technically lead initiatives across engineering teams, establishing shared interfaces and platform contracts with Autonomous Coding, CI/CD pipelines, and Code Factory.
Identify and remediate root causes of architectural bottlenecks involving latency, throughput, state management, and dependencies across distributed systems.
Assess emerging AI tooling and foundational models and recommend vendor-neutral, future-proof integration patterns.
Lead department-level technical planning, establish architectural RFC standards, and facilitate design reviews across product areas.
Set production standards for resilience, testing, and deployment; coordinate responses to high-severity incidents and lead post-incident reviews focused on preventing recurrence.
Coach and develop Senior (IC3) and Staff (IC4) engineers, cultivating a culture of craft, ownership, and psychological safety.
Use AI tools responsibly, critically review AI-generated code and architecture, follow data-handling and security protocols, and share effective AI development workflows and evaluation guardrails.