- Lead independent research projects in AI evaluation methodologies, alignment techniques, and synthetic data generation
- Design and implement novel evaluation frameworks for LLMs and agent systems that are grounded in human data
- Contribute to the academic AI community through publications and open-source contributions
- Design and conduct rigorous experiments to study AI models and systems with sound methodological approaches
- Develop scalable frameworks for systematic evaluation of model behaviours and capabilities
- Create tools and frameworks that transform research insights into practical applications
- Apply knowledge of model fine-tuning, optimization techniques, distillation, and other ML engineering practices to support research goals
- Work closely with ML engineers, data scientists, and product teams to translate research insights into practical applications
Machine LearningLLM