Graduate ML Backend Engineer — High-Performance Kernels

BYTEDANCE PTE. LTD.

Singapore

On-site

SGD 120,000 - 180,000

Full time

4 days ago
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Job summary

ByteDance is seeking talented graduates to join the Data AML mid-platform team. You will optimize the computational performance of the company's recommendation mid-platform models and lead the design of high-performance kernel libraries for general-purpose and customized kernels.

You will collaborate with business and algorithm teams to identify bottlenecks, perform full-stack performance analysis, and contribute to the mid-platform model compilation system through graph optimization, fusion,

Qualifications

  • Bachelor's or Master's in computing or related discipline.
  • Familiar with TVM, MLIR, XLA stacks; development and optimization experience.
  • Proficient in C/C++; knowledge of CPU/GPU architecture and cache mechanisms.
  • Understand deep learning frameworks and computational graphs; experience with model graph optimization.

Responsibilities

  • Optimize computational performance of ByteDance's mid-platform models; tune inference/training bottlenecks.
  • Design and develop high-performance kernel libraries for general-purpose and customized kernels.
  • Advance model compilation technologies: graph optimization, kernel fusion, scheduling, code generation.
  • Collaborate with business/algorithm teams to diagnose bottlenecks and deliver optimization solutions.

Skills

Model compilation stacks
C/C++ development
Deep learning frameworks
Independent thinking

Education

Bachelor's/Master's in computing or related

Tools

TVM
MLIR
XLA

Job description

ByteDance is seeking talented graduates to join the Data AML mid-platform team. You will optimize the computational performance of the company's recommendation mid-platform models and lead the design of high-performance kernel libraries for general-purpose and customized kernels.

You will collaborate with business and algorithm teams to identify bottlenecks, perform full-stack performance analysis, and contribute to the mid-platform model compilation system through graph optimization, fusion,

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