ML Systems Engineer, ML Acceleration

What you'll be doing:

• Performance Profiling & Optimization: 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.
• Distributed Training: Optimize distributed training pipelines using frameworks such as PyTorch Distributed.
• Kernel Development: Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.
• Data Pipeline Engineering: Optimize robust data loading pipelines that maximize training throughput.
  

What we're looking for:

• Education: Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.
• Software Engineering: Strong proficiency in Python.
• ML Frameworks: Extensive hands-on experience with PyTorch.
• ML Knowledge: Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.
• Problem Solving: Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges.