Senior Software Engineer, Onboard Autonomy

Nuro

Mountain View, California

Full Time

Posted March 10, 2026

$194k - $291k

Electric Vehicles

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Job Description

A role focused on developing and deploying onboard autonomy software for self-driving vehicles, including tasks such as navigation, obstacle avoidance, sensor integration, and real-time decision-making to ensure safe and efficient autonomous operation.

Key Responsibilities

Develop, integrate, and deploy onboard autonomy behaviors such as navigation, obstacle avoidance, lane following, docking, and interaction behaviors
Implement and maintain real-time decision-making components including behavior planning, state machines, local planning, and control interfaces
Build and optimize sensor-driven autonomy pipelines using onboard sensors like cameras, lidar, radar, IMU, wheel odometry, and GNSS
Optimize autonomy performance for latency, CPU/GPU usage, memory, and power on embedded compute platforms
Design and implement safety and fallback strategies, including health monitoring, degraded modes, watchdogs, and redundancy logic
Own the integration, debugging, profiling, logging, and validation of the autonomy stack on real hardware
Improve onboard observability through structured logs, traces, metrics, and incident review tools
Collaborate with perception, mapping, localization, controls, hardware, and systems teams to define interfaces and ship end-to-end features
Participate in field testing and root-cause analysis of autonomy issues in real deployments

Requirements

Strong software engineering skills in C and/or Rust; Python acceptable as a supporting language.
Experience shipping software that runs on-device with real-world constraints, including embedded Linux, real-time-ish systems, and performance-sensitive code.
Understanding of autonomy fundamentals such as planning, state estimation, localization, and controls, with the ability to interface these components (expertise in all not required).
Experience with robotics middleware and tooling commonly used in robotics, such as ROS, ROS 2, custom pub/sub frameworks, gRPC, DDS, etc.
Proficiency with debugging and performance tools such as gdb, lldb, perf, flamegraphs, profiling GPU workloads, and log trace analysis.
Strong testing discipline including unit tests, integration tests, simulation, Hardware-in-the-Loop (HIL) concepts, and safe rollout practices for autonomy.
Experience developing and deploying onboard autonomy behaviors such as navigation, obstacle avoidance, lane route following, docking, and interaction behaviors.
Experience implementing and maintaining real-time decision-making components including behavior planning, state machines, behavior trees, local planning, and control interfaces.
Experience building robust sensor-driven autonomy pipelines on-device using sensors such as camera, lidar, radar, IMU, wheel odometry, GNSS, including synchronization, calibration, and fault handling.
Ability to optimize autonomy performance for latency, CPU/GPU usage, memory, and power on embedded compute platforms such as NVIDIA Jetson, x86 edge boxes, or custom ECUs.
Experience designing and implementing safety and fallback strategies, including health monitoring, degraded modes, watchdogs, safe-stop, and redundancy-aware logic.
Ownership of the autonomy stack’s on-robot integration, including bring-up, debugging, profiling, logging, and release validation on real hardware.
Ability to improve onboard observability through structured logs, traces, metrics, event recording, and tools to support incident review and rapid iteration.
Experience participating in field testing and root-cause analysis of autonomy issues observed in real deployments.