Senior Staff Engineer (backend) - Road Safety/insurance

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

Senior Staff Backend Engineer for Uber's Road Safety and Insurance teams, focusing on backend platforms that process telematics, sensor, and behavioral data to enhance safety. The role involves leading cross-team initiatives, designing and evolving data ingestion and processing systems, and partnering with ML/data science teams to productionize models for crash and driving behavior detection. Emphasis on scalability, reliability, and low-latency streaming pipelines.

What you'd actually do

  1. Lead large, cross-team technical initiatives collaborating with multiple platform teams, from early design through production rollout.
  2. Builds strategic relationships with internal stakeholders (e.g. engineering leaders, product managers, designers, operations representatives), partner teams and external partners to deliver on organizational goals.
  3. Design and evolve backend systems that ingest, process, and analyze high-volume sensor and telematics data (e.g., GPS, accelerometer, gyroscope, vehicle signals and user feedbacks).
  4. Partner closely with machine learning and data science teams to productionize ML and signal-processing models for crash detection, driving behavior detection etc.
  5. Drive architectural decisions for low-latency streaming pipelines, distributed services, and offline/online data processing systems.

Skills

Required

  • BS or equivalent in Computer Science, Engineering, Mathematics, or a related field.
  • Proven experience designing and building large-scale distributed systems serving millions of users.
  • Strong computer science fundamentals: data structures, algorithms, system design, and performance optimization.
  • Demonstrated ability to lead complex projects across multiple teams or organizations.

Nice to have

  • 10+ years of professional software engineering experience, with significant backend depth.
  • Experience working with telematics, sensor data, time-series data, or signal processing systems.
  • Hands-on experience with streaming and real-time data pipelines (e.g., Kafka-like systems, stream processing frameworks).
  • Exposure to machine learning systems in production, including model integration, feature pipelines, or ML-powered decision systems.
  • Strong background in scalability engineering, data consistency, and system observability.
  • Track record of influencing architecture and technical direction across multiple teams.

What the JD emphasized

  • petabytes of telematics, sensor, and behavioral data
  • and have real-world impact
  • technical leader across teams
  • designing and building large-scale distributed systems
  • lead complex projects across multiple teams or organizations