ML Engineer, Post-Training

The agents Zoro installs draft replies, route money, extract records, and decide what a human must approve. When one of them is wrong, a real company feels it. Post-training is where that behavior gets owned. You will own the eval harnesses, the fine-tuning runs, and the routing layer that decides which model sees which task. The hardest part of the job is the judgment boundary: what the model may do alone, and what it must hand to a person. What you will do Own the eval suites that gate every model change before production Fine-tune and route models for drafting, extraction, and routing tasks Define and enforce the judgment boundary between model and human Watch production behavior and drive failure rates down week over week Lower cost per correct action without lowering the standard What we are looking for LLM systems shipped to production, not notebooks and demos Deep eval methodology: you can say exactly why a model got better Fine-tuning experience: SFT, preference tuning, or both Strong Python and strong opinions about measurement You read papers and ship code, in that order Nice to have Open-weight model deployments under real latency and cost limits Retrieval systems over messy company data Observability tooling for agent behavior in production #J-18808-Ljbffr Zoro