Sr Software Engineer

We are looking for a Senior Software Engineer to focus on Sensor Reliability Engineering, owning the observability, alerting, and automation that ensures Uber’s in-vehicle sensor data collection systems operate reliably at scale., 1. Architect Observability Systems: Design and implement monitoring infrastructure for in-vehicle sensor packages and recording pipelines, covering signal ingestion, storage, and correlation.

1. Build for Edge Constraints: Develop systems that remain performant despite hardware diversity, intermittent connectivity, and rapid fleet scaling.
2. Define Criticality Models: Establish alerting strategies that distinguish transient anomalies from systemic issues impacting sensor uptime and data yield.
3. Detect Complex Failure Modes: Design detection logic for “silent” failures, such as sensor degradation, compute saturation, or recording pipeline stalls.
4. Scale Through Automation: Design automated detection, triage, and mitigation mechanisms to eliminate manual intervention as the fleet grows.
5. Partner on Mitigation: Collaborate with Operations and Engineering to build safe, automated responses to recurring hardware and software failure scenarios.
6. Enable Observability by Design: Partner with hardware and platform teams to define the signals and data contracts required for deep-stack visibility.
7. Drive Operational Efficiency: Build technical interfaces to help Operations surface issues and Engineering diagnose and deploy mitigations rapidly (TTD/TTM).
8. Own Modern Infrastructure: Lead the deployment and evolution of fleet-wide reporting systems using Infrastructure as Code (IaC) best practices.
9. Lead Technical Strategy: Drive reliability-focused design reviews and translate operational pain points into concrete technical requirements and high-priority roadmaps.

As the technical owner for sensor reliability and observability, you will build the infrastructure that converts low-level signals into actionable intelligence and automated responses. This is a senior role requiring strong software engineering fundamentals, deep systems thinking, and the ability to drive cross-team technical direction without direct authority., 1. Proficiency in one or more of Go, Python, or C++, with experience building and operating production systems.

1. Proficiency in Linux internals and shell scripting for triaging and debugging edge devices or hardware-adjacent systems.
2. Strong software engineering fundamentals with the ability to debug across services, containers (Docker), and networking stacks.
3. Proven experience owning reliability, infrastructure, or platform systems for large-scale production workloads.
4. Experience designing and operating observability systems, including metrics, logging, alerting, and dashboarding (e.g., Prometheus, Grafana).
5. Experience defining and implementing Service Level Indicators (SLIs) and Objectives (SLOs) for system availability or data yield.
6. Deep understanding of networking protocols (TCP/IP, gRPC, or MQTT) and data handling in bandwidth-constrained or intermittent environments.
7. Track record of driving complex technical projects and architectural reviews across multiple teams from design through production., 1. Experience leading large-scope reliability or infrastructure initiatives consistent with a Senior/Staff role.
8. Deep experience with modern observability platforms (e.g., Prometheus, Grafana, ELK), especially in edge, IoT, or hardware-integrated environments.
9. Experience designing alerting strategies and criticality models that balance signal quality, noise reduction, and operational impact.
10. Strong automation mindset, including building self-healing systems for automated detection, triage, or mitigation of hardware-related failures.
11. Experience operating systems where uptime, data yield, or hardware availability are core business KPIs.
12. Proven ability to design reliability systems that remain effective as hardware platforms, software stacks, and data collection workflows evolve.
13. Knowledge of sensor data protocols (e.g., Camera, LiDAR, Radar) or hardware-to-cloud data ingestion pipelines.
14. Experience with “Grey Failure” detection and management in complex, distributed systems.
15. Background in analyzing “Fleet-level” performance metrics to identify systemic regressions across software versions or hardware revisions.