Research Engineer, Post-Training

The role Our claim is that our data improves models. You’ll build the infrastructure to prove it: fine-tuning and RL runs that isolate the effect of a dataset, evaluation that is comparable across runs, and tooling that makes the next experiment faster than the last. What you’ll own SFT and RL post-training pipelines on open models.Controlled experiments that measure what a dataset changes.Experiment tracking, checkpoint management and evaluation plumbing.Integration with customer training stacks where programs require it.Concise reports with the confounders named. What you bring Two or more years in ML engineering or research engineering.Hands-on fine-tuning of language models and running evaluations.Strong Python and systems debugging across data, compute and model behaviour.You ship reproducible pipelines. Helpful, not required Distributed training frameworks and cluster schedulers.RL algorithms for language models.