Quantitative Trader Intern, PhD

Required

• 3+ years of experience optimizing machine learning training workloads in high-performance, distributed, or large-scale computing environments.
• Deep knowledge of machine learning frameworks such as PyTorch or JAX, including their execution models, compilation paths, autograd systems, and distributed-training capabilities.
• Strong programming skills in Python and C++, with experience developing or optimizing performance-critical systems.
• Proven experience with GPU kernel development and optimization using technologies such as CUDA, Triton, CUTLASS, cuBLAS, cuDNN, or related libraries.
• Strong understanding of GPU architecture, including streaming multiprocessor execution, warp scheduling, tensor cores, and the memory hierarchy from registers through HBM.
• Experience with distributed-training technologies and communication libraries such as NCCL, FSDP, DeepSpeed, Megatron-LM, XLA, or equivalent systems.
• Proficiency with performance-analysis tools such as Nsight Systems, Nsight Compute, PyTorch Profiler, or comparable tracing and profiling platforms.
• Understanding of high-performance networking, storage, and accelerator interconnects, including technologies such as InfiniBand, RDMA, NVLink, or NVSwitch.
• Demonstrated ability to benchmark heterogeneous compute platforms and make rigorous, data-driven recommendations about performance, scalability, and cost.