Design, implement, and maintain SFT/RL post-training pipelines for agents.
Train and adapt LLMs for agent workflows, including planning.
Build evaluation/simulation environments for agents to act and be measured.
Design evaluation metrics for agent behavior; analyze traces for improvements.
Analyze training results to improve model architectures, training recipes, and datasets.
Work with distributed GPU clusters and MapReduce-style data processing.

🎯 Requirements

Hands-on experience training LLMs (pre-, fine-, or post-training).
Deep expertise in PyTorch and specialized LLM stacks (Megatron, NeMo, etc.).
LLM fundamentals: architectures, tokenization, data pipelines, batching.
Ability to own projects end to end from problem to iteration.
Product-aware mindset: translate product needs into modeling and evaluation.
3+ years of Python experience in modern ML codebases.

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