Leads the people function across recruiting, employee experience, HR compliance, performance, and workforce planning while building scalable systems and a positive workplace culture.
Develop deep learning representations and foundation models for molecular and atomistic systems, shaping training strategies and scalable research components in collaboration with physicists, chemists, and research engineers.
Develop generative models and sampling methods for molecular systems, combining machine learning, statistical mechanics, and Bayesian inference to advance simulation and exploration of physical systems.
Optimize GPU implementations of advanced deep learning architectures for molecular simulation, accelerating performance-critical workloads while preserving model accuracy, reproducibility, and scientific fidelity.
Build scalable machine learning systems and asynchronous workflows for data generation, model training, and evaluation, enabling research teams to run molecular machine learning experiments at foundation-model scale.
Advance foundation simulation models by designing experiments, implementing research architectures, building scalable training and evaluation systems, and turning scientific prototypes into production.
Build and optimize distributed computing infrastructure for machine-learning data pipelines, training, and fine-tuning, improving cluster reliability, observability, performance, and compute efficiency.
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