Senior Software Engineer

Uber Uber · Consumer · San Francisco, CA +1 · Engineering

Senior Software Engineer for Uber's Earner Trip Experience team, focusing on backend platforms, SDKs, and APIs that power seamless on-trip experiences for millions of Earners. The role involves technical leadership, architecting scalable systems, driving cross-functional collaboration, and ensuring system performance and quality. Requires experience in building highly scalable distributed systems and platform/SDK/API design, with a preferred track record in applied AI for user experience and production ML model deployment.

What you'd actually do

  1. Architect and own the backend services and SDKs that power the Earner trip lifecycle—from dispatch through completion.
  2. Create reusable frameworks and APIs that enable other Uber teams to plug into the trip experience without reinventing the wheel.
  3. Act as the primary technical point of contact for complex initiatives, aligning roadmap requirements between backend, mobile, and product stakeholders.
  4. Optimize system performance and reliability to handle peak global traffic, ensuring a 99.99% uptime for mission-critical Earner workflows.
  5. Set the bar for code quality, architectural reviews, and comprehensive testing strategies within the Tripex organization.

Skills

Required

  • backend software engineering
  • designing and building highly scalable, distributed systems
  • Platform and SDK/API design
  • cross-team and cross-functional (XFN) collaboration

Nice to have

  • Go
  • Java
  • C++
  • large-scale platform abstractions
  • microservices architecture
  • concurrency
  • consistency models
  • high-availability patterns
  • mentoring junior and mid-level engineers
  • Applied AI Experience
  • deploying and scaling Machine Learning models in production environments

What the JD emphasized

  • highly scalable, distributed systems
  • Platform and SDK/API design
  • cross-team and cross-functional (XFN) collaboration
  • Applied AI Experience
  • deploying and scaling Machine Learning models in production environments

Other signals

  • architecting highly scalable platforms, SDKs, and APIs
  • foundational infrastructure
  • building user experience leverage AI
  • deploying and scaling Machine Learning models in production environments