• Define and execute a research agenda focused on LLM evaluation and post-training, especially evaluation-driven model improvement • Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes • Develop and validate evaluation frameworks for LLM and multimodal systems, including: benchmark/task design scoring methods judge/model-assisted evaluation human evaluation protocols robustness/stress testing • Lead research on advanced evaluation domains, including long-context, cross-modal, and dynamic multi-turn evaluations • Study the effectiveness and limitations of existing evaluation techniques, and propose improved methodologies with clear validity and scalability tradeoffs • Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign • Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines • Collaborate with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies into research programs • Engage with customer technical stakeholders to understand evaluation goals, review methodologies, and provide expert recommendations • Contribute to internal benchmark datasets, evaluation frameworks, and reusable research assets • Produce high-quality technical documentation, internal research reports, and client-facing materials explaining methods, results, assumptions, and limitations • Contribute to thought leadership and best practices in LLM evaluation, post-training, and GenAI quality measurement