{"id":2010449,"url":"https://alion.io/job/nuro-software-engineer-applied-artificial-intelligence-infrastructure","title":"Software Engineer (Applied Artificial Intelligence Infrastructure)","company":{"id":22290,"name":"Nuro","domain":"nuro.ai","url":"https://alion.io/company/nuro","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":{"grade":"B","score":75,"open_postings":11,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":193930,"max":352290,"currency":"USD","period":"year","gross":null,"usd_annual":352290},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Model Distillation","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Rust","optional":false},{"name":"SFT","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Tokenization","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-08-15T00:13:54Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-13T12:03:46Z","board_verified":true,"closed_at":null,"days_open":59,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":59},"description":"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.\nMissions\nConcevoir 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.\nDévelopper 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.\nConcevoir 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.\nProfil recherché\n - 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\n- Strong programming skills in Python\n- 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\n- Strong backend and distributed systems background at scale: cloud infrastructure, service design, storage, queuing, and the judgment to build things that stay up\n- 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\n- You have built and operated LLM-based agent systems in production - tool use, orchestration, sandboxing, retrieval, memory - and you know where they break\n- You work end-to-end and do not need the problem pre-decomposed. This role has more surface than a specification\n- 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. 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