Senior Software Engineer - GenAI Infrastructure & Agent Systems for Engineering Efficiency

Who We Are

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future.

About the Team

We empower engineers to build the future of transportation by transforming how software is developed, tested, and operated using Generative AI and agentic systems.

Our team builds the AI infrastructure that enables autonomous coding agents, intelligent assistants, and autoresearch systems across the engineering lifecycle — from code generation and CI/CD to debugging, knowledge discovery, and ML model improvement.

We sit at the intersection of engineering productivity and ML advancement, building platforms and self-improving systems that accelerate both.

About the Role

As a Software Engineer in GenAI Infrastructure & Agent Systems, you will design and build platforms that significantly improve developer productivity and ML research velocity.

This role spans:

• Agent infrastructure (harnesses, sandboxes, plugin frameworks, MCP integrations) enabling reliable, production-grade AI agents
• Autoresearch systems that run experiments, evaluate results, and propose improvements to core ML models

You’ll work across the stack — from cloud infrastructure and isolation to agent memory, evaluation frameworks, and developer-facing tools.

About the Work

Build AI agent infrastructure

• Design agent orchestration, sandboxing, and isolation systems
• Develop skill/plugin frameworks and integrate internal tools (e.g., CI/CD, code, observability systems)
• Scale compute infrastructure for agent workloads (GCP, Kubernetes)

Develop autoresearch systems

• Build self-improving agents that run experiments, analyze results, and iterate
• Implement memory, feedback loops, and long-horizon reasoning
• Integrate with ML training, evaluation, and data pipelines

Create AI-powered engineering tools

• Build assistants for support, triage, and workflow automation
• Develop tools for code generation, debugging, testing, and PR review
• Detect and resolve performance and reliability issues automatically

Improve quality and developer workflows

• Design evaluation and observability systems for agent performance
• Integrate agents into CI/CD for testing, failure attribution, and remediation
• Build knowledge sys