ML Systems Engineer, ML Acceleration

Motional; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

Motional is seeking a Machine Learning Systems Engineer to join the ML Acceleration team in Singapore. You will optimize training speed, reduce time-to-convergence, and build high-performance data pipelines and GPU kernels.

The role focuses on scaling large distributed models across compute resources and improving overall throughput. Ideal candidates have strong Python skills, deep PyTorch experience, and a solid foundation in ML concepts, architectures, and optimization strategies.

Qualifications

• 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 ML model execution during training and inference; solid ML fundamentals.

Responsibilities

• Performance profiling to identify bottlenecks in data loading, gradient computation, and communication.
• Optimize distributed training pipelines using PyTorch Distributed.
• Design and maintain high-performance GPU kernels in Triton or CUDA.
• Design robust data loading pipelines to maximize training throughput.

Skills

Python

Problem Solving

Analytical thinking

Education

Bachelor’s/Master’s/PhD in CS or CE

Tools

Nsight

PyTorch Profiler

PyTorch

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.