Senior Software Engineer, Onboard Autonomy

Who We Are

Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides.

Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.

With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected.

Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors.

About the Role

We’re looking for an Autonomy Engineer focused on onboard autonomy—the software that runs on the robot/vehicle/embedded computer and makes real-time decisions using onboard sensors and compute. You’ll build and ship reliable autonomy features that operate under tight latency, compute, and safety constraints in the real world.

What You’ll Do

• Develop, integrate, and deploy onboard autonomy behaviors (e.g., navigation, obstacle avoidance, lane/route following, docking, interaction behaviors).
• Implement and maintain real-time decision-making components: behavior planning, state machines/behavior trees, local planning, and control interfaces.
• Build robust sensor-driven autonomy pipelines on-device (camera, lidar, radar, IMU, wheel odometry, GNSS), including synchronization, calibration hooks, and fault handling.
• Optimize autonomy performance for latency, CPU/GPU usage, memory, and power on embedded compute (e.g., NVIDIA Jetson, x86 edge boxes, custom ECUs).
• Design and implement safety and fallback strategies: health monitoring, degraded modes, watchdogs, safe-stop, and redundancy-aware logic.
• Own the autonomy stack’s on-robot integration: bring-up, debugging, profiling, logging, and release validation on real hardware.
• Improve onboard observability: structured logs, traces, metrics, event recording, and tools to support incident review and rapid iteration.
• Collaborate with perception, mapping/localization, controls, hardware, and systems teams to define clear interfaces and ship end-to-end features.
• Participate in field testing and root-cause analysis of autonomy issues seen in real deployments.

Required Experience

• 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 (embedded Linux, real-time-ish systems, performance-sensitive code).
• Understanding of autonomy fundamentals: planning, state estimation/localization, controls, and how they interface (you don’t need to be an expert in all).
• Experience with robotics middleware and tooling (commonly ROS/ROS 2, custom pub/sub frameworks, gRPC, DDS, etc.).
• Proficiency with debugging and performance tools (e.g., gdb/lldb, perf, flamegraphs, profiling GPU workloads, log/trace analysis).
• Strong testing discipline: unit/integration tests, simulation/HIL concepts, and safe rollout practices for autonomy.

Nice to Have

• Experience with behavior trees (e.g., BehaviorTree.CPP), hierarchical state machines, or mission/task planning.
• Practical experience with local planners (trajectory rollout, MPC, sampling-based methods) and real-time control loops.
• Sensor fusion experience (EKF/UKF), time sync, calibration, and handling intermittent sensors.
• Experience with mapping and localization stacks (scan matching, visual-inertial odometry, SLAM, map-based localization).
• Familiarity with safety standards/processes (e.g., ISO 26262 concepts, FMEA, hazard analysis) depending on domain.
• Experience deploying autonomy to fleets: OTA updates, versioning, configuration management, and field telemetry.
• Experience in inference optimization