Machine Learning Systems Engineer

Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time. Optimize distributed training pipelines using frameworks such as PyTorch Distributed. Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads. Optimize robust data loading pipelines that maximize training throughput. Requirements Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline. Strong proficiency in Python. Extensive hands-on experience with PyTorch. Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes. Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges. Core Competencies Demonstrates expertise in optimizing machine learning workflows through advanced techniques in Python and PyTorch, with a strong foundation in GPU kernel design and data loading strategies to enhance training efficiency. #J-18808-Ljbffr