Senior Staff Engineer (Backend) - Road Safety/Insurance

Senior Staff Engineer (Backend) - Road Safety/Insurance Department: Engineering Team: Backend Location: San Francisco, United States Type: Full-Time **About the Role** Uber’s Road Safety and Insurance Engineering teams sit at the intersection of real-world mobility, large-scale data systems, and safety-critical decision making. Our systems process **petabytes of telematics, sensor, and behavioral data** that help detecting crashes, assess driving risk and ultimately make every trip safer. Every Uber trip, across ridesharing, delivery, and emerging mobility products, is made safer by the systems this role helps build. With **#StandForSafety** as a company-wide priority, this is a rare opportunity to shape backend platforms that operate at massive scale **and** have real-world impact. We are looking for a **Senior Staff Backend Engineer** to provide technical leadership across **Road Safety and Insurance engineering teams**, leading complex, multi-team initiatives that span real-time data ingestion, signal processing, machine learning–powered risk assessment, and highly reliable backend platforms. **What You'll Do** As a Senior Staff Engineer, you will operate as a **technical leader across teams**, not just within a single codebase. You will: 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**. 6. Anticipate and