• responsible for development and execution of the research strategy and overall management of the research portfolio for the Ultrasound AI Center of Excellence • lead a cross functional team to develop the research strategy • ensure the research portfolio supports the business focus • engage and support clinical and technical sites research in their studies • contribute in technical development to support research needs • collaborate with various stakeholders to develop comprehensive research strategies and plans • prioritize and resource allocation across the full research portfolio • seek out academic partners well-suited for a GEHC partnership • define product strategic research needs from business planning sessions • respond to and assess incoming research proposals • develop NPI specific evidence strategies and plans including evaluation plans • partner with the Research Program Integrator (RPI) to support execution on research studies • support utilization of research results and assess their impact

• Conduct fundamental and innovative development in low-cost yet powerful vision-language models (VLM), unified models, automatic model compression, optimization and deployment on cloud and edge. • Design or implement state-of-the-art techs on model compression, inference speedup, deployment on hardwares, tool automation. • PoC for various vision+text, generation relevant tasks (VQA, captioning, understanding, etc) and hardwares. • Contribute to library and tool development to support business; or Publish influential research in top-tier conferences and journals.

• Act as the voice of the buyside customer and run regular user sessions to shape and execute AI product strategies that directly address user needs and business goals. • Work side-by-side with engineering, design, and product marketing to build, launch, and optimize innovative AI-first solutions tailored to our customers. • Analyze market trends, emerging AI capabilities, and competitor moves to identify where we can leap ahead. • Lead the end-to-end product development lifecycle, from ideation and experimentation through to launch, post-launch analysis, and iteration.

• Own the medical modeling roadmap at Atria: identify the model families and training approaches most likely to win on our problems, and build the models that prove it. The bar is "moves the clinical needle", not "places on a leaderboard". • Survey the open-source landscape (general-purpose and clinical/biomedical foundation models, vision and multimodal backbones, specialized medical models) and make defensible recommendations on what to fine-tune, distill, or build on. • Design and validate novel architectures where existing approaches fall short, particularly for longitudinal multi-modal data and the foundation-model direction. • Lead fine-tuning and post-training work: SFT, LoRA/QLoRA and other PEFT methods, DPO and related preference-tuning approaches, RLHF/RLAIF where appropriate, continued pre-training, and distillation. • Curate high-quality training datasets from Atria's clinical data: sampling, labeling strategy, deduplication, contamination checks, train/test hygiene, and PHI-safe handling throughout. • Design and run rigorous evaluation: held-out clinical benchmarks, comparison against frontier closed-source baselines, calibration analysis, subgroup performance, and the ablations that tell you which design choices actually earned their keep. • Run training and evaluation on appropriate infrastructure (single-node and multi-GPU), with proper experiment tracking, reproducible configs, and the kind of logging that lets your future self understand what your past self did. • Stay current with the literature: training methods, fine-tuning techniques, medical foundation models, multimodal architectures. Synthesize what's actually relevant for Atria rather than reporting every new arXiv preprint as a strategic threat. • Collaborate with clinicians on what "good" looks like for each model, and with research partners on joint training and evaluation work.