Staff Software Engineer, Test Platform

SoFi SoFi · Fintech · San Francisco, CA · Infrastructure

Staff Software Engineer on the Test Platform team, focusing on building a greenfield project for autonomous testing in an AI-driven SDLC. The role involves integrating AI for automated test generation, selection, and failure analysis, and researching/prototyping AI/ML tools to enhance developer productivity and test coverage.

What you'd actually do

  1. Provide technical leadership for initiatives in Testing and Reliability, with a focus on integrating AI-driven automation and autonomous testing practices.
  2. Collaborate with product engineering teams to understand requirements and design platform capabilities that are efficient, robust, and developer-friendly.
  3. Architect and implement solutions that accelerate integration, load, performance, and chaos testing—including the use of AI for automated test generation, selection, and failure analysis.
  4. Deliver software that enables seamless testing and operation of backend systems in cloud-native, containerized, and CI/CD environments, supporting shift-left and continuous delivery.
  5. Research, prototype, and productionize AI/ML tools to enhance developer productivity, test coverage, and test maturity.

Skills

Required

  • software development experience
  • cloud environment (AWS)
  • containers (e.g., Docker, Kubernetes)
  • cloud-native technologies
  • service meshes (e.g., Istio, Envoy)
  • software design principles
  • distributed systems architecture
  • Java
  • Kotlin
  • Python
  • Go
  • automated testing strategies
  • testing in production
  • test tenancy
  • API mocking
  • traffic capture
  • routing and playback technologies
  • problem-solving skills
  • strategic thinking
  • project ownership
  • communication skills
  • collaboration skills

Nice to have

  • load testing (e.g., Locust, Artillery)
  • E2E testing (e.g., Cypress)
  • failure injection and chaos testing (Gremlin, AWS FIS) technologies
  • monitoring and logging (e.g. Datadog, Elastic, Splunk)
  • CI/CD pipelines and tools (e.g., Argo, GitLab CI/CD)
  • security and compliance requirements in cloud environments

What the JD emphasized

  • AI-driven SDLC
  • autonomous testing
  • AI for automated test generation, selection, and failure analysis
  • AI/ML tools to enhance developer productivity, test coverage, and test maturity

Other signals

  • AI-driven SDLC
  • autonomous testing for AI
  • AI for automated test generation, selection, and failure analysis
  • Research, prototype, and productionize AI/ML tools