Proxima

Engineer

• Profile and optimize training and inference for structural and generative models, including transformers, diffusion, and geometric deep learning
• Write and tune custom kernels using CUDA and Triton
• Use compilers such as torch.compile, TensorRT, and XLA when beneficial
• Scale distributed training across 32–64 nodes using FSDP, DeepSpeed, tensor parallelism, pipeline parallelism, and mixed precision
• Reduce inference cost by optimizing memory scaling for large complexes, improving diffusion sampling efficiency, batching ragged inputs, and maximizing throughput across up to 1000 GPUs
• Manage GPU cluster efficiency on GCP, focusing on scheduling, utilization, spot strategy, and cost reporting
• Develop benchmarks and profiling tools for the research team
• Help develop Proxima’s AI and data-generation platform for proximity therapeutics and protein-interaction discovery

Principal ML Performance Engineer – GPU Optimization