Software Engineer, Applied AI Infrastructure

Job description

Job responsibilities

• Build the closed-loop measurement layer that tells us, per workflow, whether agent output is accepted, reverted, or overridden — and use it to decide where autonomy expands and where it gets pulled back.
• Take the autoresearch loop from assisted to unattended for a bounded class of experiments, including the eval and confidence machinery required to run it without a human in the loop.
• Design the isolation and permissioning model that lets agents act on production repositories and infrastructure with an auditable record of what they did and why.

Job requirements

• 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.
• 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…
• 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,…
• You have built and operated LLM-based agent systems in production — tool use, orchestration, sandboxing, retrieval, memory — and you know where they break.
• Strong backend and distributed systems background at scale: cloud infrastructure, service design, storage, queuing, and the judgment to build things that stay up.
• Strong programming skills in Python.
• You are opinionated about evaluation. You have argued with someone about whether a benchmark measured anything real, and you were right.
• You work end-to-end and do not need the problem pre-decomposed. This role has more surface than a specification.