Software Engineer, Applied AI Infrastructure

Nuro•Mountain View, CA

9h•$193,930 - $352,290

About The Position

Nuro is seeking a Software Engineer for Applied AI Infrastructure to build the platform that lets AI agents operate autonomously within Nuro's engineering organization. The team's mandate is to amplify the output of every engineer and researcher at Nuro by 100x, focusing on creating a trustworthy and rigorous closed-loop evaluation system for AI work. This role involves developing the agent platform, autoresearch infrastructure, and agent-powered tooling across the engineering lifecycle. The position offers direct access to compute, systems being automated, engineering leadership, and the CEO, with significant decision-making authority. Key areas of focus include closed-loop evaluation for agent trustworthiness, the agent platform for safe operation against real systems (orchestration, sandboxing, tool frameworks, memory, identity, permissioning, observability), and autoresearch infrastructure to automate the research loop (hypothesis formation, experimentation, evaluation, and proposal generation). The role also involves building agent-powered tooling for code generation, review, debugging, test attribution, knowledge retrieval, and triage, with potential involvement in post-training models.

Requirements

3+ years of software engineering experience (or 2+ with a Master's) in computer science, engineering, or equivalent practical experience.
Deep, current understanding of LLM research, including model training from scratch (data, tokenization, architecture, pretraining dynamics, post-training stack: SFT, preference optimization, RL) and reasoning about training decisions' impact on model behavior.
Understanding of inference under the hood: attention, KV-cache, batching, scheduling, quantization, speculative decoding, prefix caching, context handling, and their trade-offs.
Experience building and operating LLM-based agent systems in production (tool use, orchestration, sandboxing, retrieval, memory).
Strong backend and distributed systems background at scale (cloud infrastructure, service design, storage, queuing).
Strong programming skills in Python.
Opinionated about evaluation and able to critically assess benchmark effectiveness.
Ability to work end-to-end without pre-decomposed problems.
Hands-on post-training or fine-tuning experience (SFT, preference optimization, RL, distillation) with required evaluation work.
Experience with ML training or research infrastructure (experiment orchestration, evaluation pipelines, hyperparameter search, data pipelines).
Experience running inference serving, cost, or capacity at meaningful scale.
Familiarity with agent architecture patterns (planning, reflection, long-horizon memory, multi-agent coordination).
Experience with open tool-integration protocols, plugin or skill frameworks, and model-routing or gateway layers.
Background in developer experience or platform engineering, observability, or security isolation.

Nice To Haves

Staff-level candidates should bring correspondingly deeper scope and ownership.
Go, C++, or Rust experience in addition to Python.

Responsibilities

Build the closed-loop measurement layer to track agent output acceptance, reversion, and overrides per workflow.
Take the autoresearch loop from assisted to unattended for a bounded class of experiments, including necessary evaluation and confidence machinery.
Design the isolation and permissioning model for agents to act on production repositories and infrastructure with an auditable record.
Develop and operate LLM-based agent systems in production, including tool use, orchestration, sandboxing, retrieval, and memory.
Build and maintain backend and distributed systems for cloud infrastructure, service design, storage, and queuing.
Implement agent-powered tooling for code generation, review, debugging, test and CI failure attribution, knowledge retrieval, and triage.
Potentially contribute to post-training models where internal workload justifies it.