Teams at SandboxAQ

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Design, scale, and productionize ML and physics-based scientific code for drug and materials discovery. Transition prototypes to robust products, orchestrate distributed GPU training, implement hardware optimizations, and support model lifecycle and integration into large-scale simulation frameworks.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large-scale simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, and support models throughout the product lifecycle to accelerate in silico drug and materials discovery.