Define and lead LLM agent research initiatives
Design agent architectures, reasoning, planning, tool-use, and multi-agent frameworks
Develop post-training recipes and RL methods for agent capabilities
Train models on large high-quality datasets with high-end GPUs
Build agent harness for reliable, complex long-horizon tasks
Create benchmarks and evaluations for agent correctness and safety

🎯 Requirements

Hands-on experience developing LLM-based agents or foundation models
Post-training, RL, reward modeling, synthetic data, agent environment design
Publications at top venues or impactful open-source contributions
Proficient in Python and PyTorch
Knowledge of Transformers, DeepSpeed, Megatron, agent/RL frameworks
Cloud computing for efficient training and deployment

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