AI & HPC Systems Engineering Manager

KLA Corporation

Singapore

On-site

SGD 180,000 - 240,000

Full time

12 days ago
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Job summary

KLA Corporation is seeking an System Design Engineering Manager specializing in AI Systems and HPC. You will lead a team of engineers and system architects to develop a platform for DL training and inference, defining the technical roadmap for DL/AI within the HPC domain.

You will collaborate with hardware and software teams to optimize AI infrastructure, ensure seamless integration with HPC clusters, and drive performance benchmarking across large-scale workloads.

Qualifications

  • Proven track record managing engineering teams in AI/HPC settings.
  • Experience with TensorFlow, PyTorch and HPC tooling (Slurm, OpenMPI).
  • Strong leadership, decision-making and communication skills.

Responsibilities

  • Lead engineers and system architects to build DL training/inference platform.
  • Define vision, strategy and roadmap for DL/AI in HPC.
  • Collaborate with hardware/software teams to design/optimize AI infrastructure.
  • Ensure AI workloads integrate with HPC clusters.
  • Oversee GPU-based cluster management and scalability.
  • Drive performance improvements and benchmark AI models on HPC.
  • Communicate progress and risks to leadership.
  • Mentor and develop team members.
  • Partner with product managers/researchers to align AI work with business goals.
  • Deliver high-quality, scalable AI infrastructure solutions.

Skills

Proven leadership
AI frameworks
Leadership communication

Tools

Docker
Kubernetes
Slurm
OpenMPI
CUDA

Job description

KLA Corporation is seeking an System Design Engineering Manager specializing in AI Systems and HPC. You will lead a team of engineers and system architects to develop a platform for DL training and inference, defining the technical roadmap for DL/AI within the HPC domain.

You will collaborate with hardware and software teams to optimize AI infrastructure, ensure seamless integration with HPC clusters, and drive performance benchmarking across large-scale workloads.

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