Senior Software Engineer – Genai Infrastructure & Agent Systems for Engineering Efficiency

Nuro Nuro · Robotics · Mountain View, CA · Offboard Infrastructure

This role focuses on building AI agent infrastructure and autoresearch systems to enhance developer productivity and ML research velocity within an engineering context. It involves designing and implementing agent orchestration, sandboxing, skill frameworks, and self-improving agents that run experiments and iterate on ML models. The role also includes creating AI-powered engineering tools for code generation, debugging, and improving quality and developer workflows through evaluation and observability systems.

What you'd actually do

  1. Design agent orchestration, sandboxing, and isolation systems
  2. Develop skill/plugin frameworks and integrate internal tools (e.g., CI/CD, code, observability systems)
  3. Scale compute infrastructure for agent workloads (GCP, Kubernetes)
  4. Build self-improving agents that run experiments, analyze results, and iterate
  5. Implement memory, feedback loops, and long-horizon reasoning

Skills

Required

  • 5+ years of experience and a Bachelor’s or 4+ years of experience and a Master’s Degree in Computer Science, software engineering, a related field, or equivalent practical experience
  • Strong programming skills in Python, C++, or Go, with experience building scalable, reliable systems
  • Solid background in cloud infrastructure (GCP/AWS), Kubernetes, CI/CD, and developer tooling

Nice to have

  • Experience with LLM/agent systems in production (tool use, orchestration, sandboxing)
  • Familiarity with autonomous agent patterns (memory, reflection, planning)
  • Experience with MCP, plugin/skill frameworks, or similar architectures
  • Background in ML infrastructure (training pipelines, evaluation, experimentation)
  • Experience in DevEx/platform engineering, observability, or security isolation patterns

What the JD emphasized

  • production-grade AI agent systems
  • autonomous, self-improving systems
  • ML-driven workflows
  • systems thinking across infrastructure, memory, and tooling
  • work end-to-end
  • LLM/agent systems in production
  • autonomous agent patterns
  • ML infrastructure

Other signals

  • AI agent infrastructure
  • Autoresearch systems
  • AI-powered engineering tools
  • developer productivity
  • ML research velocity