ML Systems Engineer, ML Acceleration

MOTIONAL SINGAPORE PTE. LIMITED; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

MOTIONAL SINGAPORE PTE. LIMITED is seeking a Machine Learning Systems Engineer to advance our ML acceleration efforts. You will optimize data loading, gradient computation, and communication to speed up large-scale model training and convergence.

You will work at the intersection of ML research and high-performance systems, focusing on speed, cost, and reliability to scale frontier models efficiently.

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or related field.
• Strong proficiency in Python.
• Extensive hands-on experience with PyTorch.
• Experience optimizing ML model execution during training and inference; strong ML concepts.
• Problem solving with data-driven approach and bias for action.

Responsibilities

• Performance profiling & optimization to reduce step time and improve throughput.
• Distributed training pipeline optimization with PyTorch Distributed.
• Design and maintain high-performance GPU kernels in Triton or CUDA.
• Build robust data loading pipelines to maximize training throughput.

Skills

Python

PyTorch

Distributed Training

Kernel optimization

Profiling/Performance

Education

Bachelor/Master/PhD in CS/CE

Tools

Nsight

PyTorch Profiler

CUDA

Triton

Job description

Mission Summary:

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models.

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.