Develops methods for AI agents to self-improve post-training through prompt optimization, continual learning from long-horizon tasks, hypothesis testing, and scalable experiments. Requires expertise in LLMs, agent frameworks, and impactful research track record.
Pioneers post-training techniques to enhance LLMs for agentic systems, focusing on tool-use, continuous updates, synthetic data infrastructure, and capability evaluations. Requires Python/PyTorch proficiency, post-training expertise, and proven research impact.
Develops methods for AI agents to self-improve post-training through prompt optimization, continual learning from long-horizon tasks, hypothesis testing, and scalable experiments. Requires expertise in LLMs, agent frameworks, and impactful research track record.
Pioneers post-training techniques to enhance LLMs for agentic systems, focusing on tool-use, continuous updates, synthetic data infrastructure, and capability evaluations. Requires Python/PyTorch proficiency, post-training expertise, and proven research impact.