Machine Learning Systems Engineer

Jobtailor; Massachusetts; On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor in Massachusetts seeks an experienced ML engineer to optimize training and inference across large models. You will profile data loading and kernel performance, implement fusion, tiling, and sharding, and collaborate with distributed training teams.

Candidates should excel in Python and PyTorch, have deep ML fundamentals, and demonstrate strong analytical problem-solving with a bias for action. Experience with CUDA or Triton is preferred, with a track record of throughput improvements.

Qualifications

• Advanced knowledge of Python and PyTorch for ML workloads.
• Experience optimizing training and inference execution.
• Strong problem-solving and data-driven approach.

Responsibilities

• Profile and optimize data loading to maximize throughput.
• Develop high-performance GPU kernels in CUDA/Triton.
• Optimize distributed training pipelines with PyTorch Distributed.
• Implement kernel fusion, sharding, and tiling to reduce step time.

Skills

Python

PyTorch

Data loading

Performance optimization

Analytical thinking

Education

Bachelor's/Master's/PhD in CS/CE or related

Tools

Nsight

PyTorch Profiler

CUDA

Triton

Job description

• 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.