• Researching and developing advanced healthcare informatics solutions with a specialization in Agentic AI. • Explore applications of Agentic and Generative AI in healthcare. • Develop, analyze, and collaborate on agentic AI projects. • Work closely with cross-functional teams to drive innovative research and practical implementation in healthcare environments.

• Scientifically direct the design and training of large-scale, state-of-the art deep learning systems • Develop novel architecture and training paradigms to lead the industry in unsolved scientific problems • Collaborate with content experts from other domains (e.g., chemistry, physics, biology) to enable innovative feature-engineering and novel cross-disciplinary approaches • Actively contribute to top-tier ML conferences and journals and attend core ML conferences to stay connected with the community and current trends

• Conduct foundational pre-training for LLMs and Multi-Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi-nodes & thousands of NVIDIA GPUs • Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers to enhance model intelligence and multi-modal understanding • Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines for efficient pre-training • Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency • Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs • Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms

• Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers • Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers • Source, filter, and curate massive-scale textual and multi-modal datasets • Independently and collaboratively execute experiments, analyze results, and refine training methodologies • Investigate, debug, and eliminate bottlenecks in model efficiency • Contribute to the advancement of distributed training systems