Join a world-class team of scientists, ML researchers, and engineers working together to make the physical microcosm predictable and reshape the future of drug discovery. We're looking for a rare individual who thrives at the intersection of cutting-edge deep learning architectures and high-performance computing.
Requirements
- Implement state-of-the-art Graph Transformers, GNNs, and similar geometric deep learning architectures into production-ready pipelines.
- Drive end-to-end performance β from high-level implementations in PyTorch / JAX down to hand-tuned CUDA kernels β to extract maximum throughput, minimize memory footprints, and optimize GPU compute bubbles.
- Help scale training and inference workloads across thousands (and eventually tens of thousands) of GPUs, maximizing FLOPs, saturating caches, and pushing hardware to its limits.
- Collaborate closely with scientists and ML researchers to identify, evaluate, and develop novel architectures with superior inductive biases for molecular modeling.
- Simulate molecular systems with unprecedented speed and accuracy β enabling breakthroughs in drug design, protein modeling, and beyond.
- Utilize generative coding tools to accelerate work and ultimately automate optimization workflows.
Benefits
- Competitive salary and benefits package
- Opportunity to work on cutting-edge projects and technologies
- Collaborative and dynamic work environment