Software Engineer, Applied AI Infrastructure

Experteer Overview As part of Nuro’s Frontier models team, you will help ensure the trustworthiness and capability of a production-scale agent system. You will work on closed-loop evaluation, an agent platform, and autoresearch infrastructure to advance autonomous agents running safely in real environments. This role focuses on measurable outcomes, robust experimentation, and auditable decision-making to enable safe, scalable autonomy. You will collaborate closely with engineering leadership to shape how AI agents operate and how their outputs are governed. Compensation / Benefits * Build the closed-loop measurement layer to track per-workflow acceptance, reversion, and overrides * Advance the autoresearch loop from assisted to unattended for a defined class of experiments with evaluation and confidence tooling * Design an isolation and permissioning model for agents acting on production repositories and infra with auditable records Tasks * 3+ years of software engineering experience (or 2+ with a Master) in CS or engineering * Deep, current knowledge of LLMs and how models are trained (data, tokenization, training dynamics, post-training strategies) * Strong backend and distributed systems experience at scale * Proficiency in Python; experience with Go, C++, or Rust is a plus * Hands-on post-training or fine-tuning experience (SFT, RLHF, distillation) and evaluation work * Experience building and operating LLM-based agent systems in production (tool use, orchestration, memory, retrieval, sandboxing) * Experience with ML training/inference infrastructure, experiment orchestration, and evaluation pipelines * Familiarity with agent architecture patterns (planning, reflection, long-horizon memory, multi-agent coordination) Key requirements * base pay * annual performance bonus * equity * competitive benefits package

(or 2+ with a Master) in CS or engineering, * Deep, current knowledge of LLMs and how models are trained (data, tokenization, training dynamics, post-training strategies) * Strong backend and distributed systems experience at scale * Proficiency in Python; experience with Go, C++, or Rust is a plus * Hands-on post-training or fine-tuning experience (SFT, RLHF, distillation) and evaluation work * Experience building and operating LLM-based agent systems in production (tool use, orchestration, memory, retrieval, sandboxing) * Experience with ML training/inference infrastructure, experiment orchestration, and evaluation pipelines * Familiarity with agent architecture patterns (planning, reflection, long-horizon memory, multi-agent coordination) Key requirements * base pay * annual performance bonus * equity * competitive benefits package