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Design, build, and operate backend services for ride planning, vehicle matching, and real-time trip execution. Integrate partner APIs, build telemetry pipelines for live vehicle state, and own feature lifecycle from requirements through launch. Ensure reliability and performance of distributed systems used in production on road.

Develop and deploy onboard autonomy software for self-driving vehicles, including real-time decision-making, behavior planning, and performance optimization.

The role involves developing and maintaining performance benchmarking infrastructure, ensuring platform reliability, and designing data pipelines, while collaborating across teams and driving technical vision.

The Sr. Software Engineer will own the perception data infrastructure for autonomous vehicles, dealing with large 3D datasets and ensuring high data quality for ML models. Responsibilities include architecting core data platforms, optimizing real-time data rendering, and collaborating across functions to enhance engineering efficiency.

Design and build safe, auditable agent and autoresearch infrastructure: closed-loop evaluation, isolation and permissioning, experiment orchestration, and large-scale inference/training pipelines. Enable autonomous research loops, evaluator and confidence machinery, and agent runtime features (orchestration, sandboxing, tool frameworks, memory, retrieval). Work end-to-end with engineers to deploy trustworthy LLM-agent capabilities against production systems.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch and streaming), ML workflows, observability, reliability, and on-call incident response to keep autonomy model training running efficiently.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch & streaming), ML workflows, observability, alerting, and on-call/incident response to ensure reliable, cost-effective model training and releases.

Design, build, and maintain ML infrastructure for model lifecycle: training, optimization, validation, deployment, inference, and observability. Own ML inference and compiler platforms, develop scalable ML pipelines, continuous testing/monitoring, and support deployment to diverse hardware for autonomous vehicles.

Build and maintain core ML infrastructure: design and implement ML training, optimization, validation, and deployment pipelines; operate an in-house ML inference and compiler platform; implement testing, monitoring, and observability for model lifecycles; collaborate with cross-functional teams to deploy optimized models to vehicle hardware.

Join Nuro as a Senior Software Engineer in Offboard Infrastructure, focusing on building platforms for data management, simulation, and technical services in autonomy technology.