Software Engineer (Applied Artificial Intelligence Infrastructure)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Aug 15, 2026. Nuro scores B on the Alion truth index.
Join Nuro, a leading robotics company, as a Software Engineer focused on Applied Artificial Intelligence Infrastructure. In this role, you will work on making our agent system trustworthy, autonomous, and significantly more capable. You will be responsible for building a closed-loop evaluation system, developing an agent platform, and automating the research loop. Additionally, you will create agent-powered tooling across the engineering lifecycle and contribute to post-training model work.
Missions
- Concevoir et construire un système d'évaluation en boucle fermée pour les agents autonomes, permettant de mesurer l'acceptation, le retour en arrière et les taux de contournement par flux de travail.
- Développer l'infrastructure d'autorecherche pour automatiser le cycle de recherche, y compris l'évaluation et la mécanique de confiance requises pour fonctionner sans intervention humaine.
- Concevoir le modèle d'isolation et de permission qui permet aux agents d'agir sur les dépôts de production et l'infrastructure avec un enregistrement auditable de ce qu'ils ont fait et pourquoi.
Profil recherché
- Hands-on post-training or fine-tuning experience - SFT, preference optimization, RL, distillation - including the evaluation work required to know whether it helped- You are opinionated about evaluation. You have argued with someone about whether a benchmark measured anything real, and you were right
- Strong programming skills in Python
- 3+ years of software engineering experience (or 2+ with a Master's) in computer science, engineering, or equivalent practical experience. Staff-level candidates should bring correspondingly deeper scope and ownership
- Strong backend and distributed systems background at scale: cloud infrastructure, service design, storage, queuing, and the judgment to build things that stay up
- You know what happens under the hood at inference. Attention and KV-cache behavior, batching and scheduling, quantization, speculative decoding, prefix caching, context handling, and how each trades off latency, throughput, and cost
- You have built and operated LLM-based agent systems in production - tool use, orchestration, sandboxing, retrieval, memory - and you know where they break
- You work end-to-end and do not need the problem pre-decomposed. This role has more surface than a specification
- Deep, current taste in LLM research. You understand how a model is trained from scratch - data, tokenization, architecture, pretraining dynamics, the full post-training stack of supervised fine-tuning, preference optimization, and RL - and you can reason about what a training decision does to model behavior. You follow the literature because you want to, not because it is on a roadmap
- Experience running inference serving, cost, or capacity at meaningful scale
- Experience with open tool-integration protocols, plugin or skill frameworks, and model-routing or gateway layers
- Go, C++, or Rust experience in addition to Python
- Familiarity with agent architecture patterns: planning, reflection, long-horizon memory, multi-agent coordination
- Background in developer experience or platform engineering, observability, or security isolation
- Experience with ML training or research infrastructure: experiment orchestration, evaluation pipelines, hyperparameter search, data pipelines