Staff Software Engineer (backend) - Road Safety

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

Staff Software Engineer (Backend) - Road Safety at Uber. This role focuses on building and maintaining backend systems that process large volumes of telematics and sensor data in real-time to improve road safety. It involves designing long-lasting engineering artifacts, solving complex problems, navigating technical debt, and championing engineering excellence. While GenAI integration is mentioned as an innovation, the core of the role is in large-scale distributed systems and data processing for safety-critical applications.

What you'd actually do

  1. Design and lead the development of long-lasting engineering artifacts that reduce complexity and improve developer velocity across the organization.
  2. Solve messy, high-impact problems by building and maintaining backend systems that process petabytes of telematics and sensor data in real-time.
  3. Navigate technical debt and legacy complexity while making intelligent bets on high-impact innovations like GenAI integration.
  4. Foresee architectural gaps and opportunities 1–2 years out, working with leadership to address them before they become blockers.
  5. Champion engineering excellence by defining standards for code health, testing, and observability, even when time is tight and pressure is high.

Skills

Required

  • Designing and building large-scale distributed systems serving millions of users
  • backend development
  • software engineering fundamentals (data structures, algorithms, and system design)
  • leading complex technical projects that span multiple teams or organizational areas

Nice to have

  • 8+ years of professional software engineering experience
  • significant backend depth
  • Domain knowledge in mobile telematics, sensor data, time-series data, or signal processing
  • Experience with streaming and real-time data pipelines or stream processing frameworks
  • Systems thinking
  • track record of influencing technical direction across disparate organizations
  • Adaptability in navigating constant change
  • go-get-it attitude toward resolving technical friction

What the JD emphasized

  • reduce complexity
  • improve developer velocity
  • messy, high-impact problems
  • petabytes of telematics and sensor data in real-time
  • technical debt
  • legacy complexity
  • GenAI integration
  • architectural gaps
  • code health
  • testing
  • observability
  • ship practical solutions at speed
  • complex codebases
  • rigorous design reviews
  • mentorship
  • technical goals
  • long-term system reliability