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.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonPyTorchCUDATritonkernel fusionshardingtilingdistributed trainingdata loading optimizationmachine learning

Soft Skills

analytical skillsproblem-solving skillsbias for actiondata-driven approach

Certifications

Bachelor’s degreeMaster’s degreePhD