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.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, support models through full product lifecycle, and integrate complex datasets and ML models into external-facing drug and materials discovery products.

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.