Large Scale Distributed Training Specialist

1 week ago

Singapore beBeeDistributedTraining Full time $125,000 - $175,000
Distributed Training & Inference Optimization Specialist

We are looking for a skilled specialist to maximize the performance and efficiency of large-scale training and inference workloads on our GPU clusters.

Key Responsibilities:
  1. Optimize LLM training frameworks: Maximize GPU utilization and reduce training time using PyTorch, DeepSpeed, Megatron-LM, and FSDP.
  2. Profile and optimize distributed training bottlenecks: Identify and resolve NCCL issues, CUDA kernel efficiency, and communication overhead.
  3. Implement and tune inference optimizations: Achieve low-latency and high-throughput LLM serving using quantization, dynamic batching, KV caching, vLLM, TensorRT-LLM, Triton, and SGLang.
  4. Collaborate with infrastructure teams: Improve GPU cluster scheduling, resource allocation, and fault tolerance for large-scale training jobs.
  5. Develop benchmarking tools: Measure and improve training throughput, memory efficiency, and inference latency.
  6. Research and apply cutting-edge techniques: Optimize LLM performance using mixture-of-experts and speculative decoding.
Requirements:
  • Hands-on experience: 3+ years in GPU-accelerated ML training and inference optimization, preferably for LLMs or large-scale deep learning models.
  • Deep expertise: PyTorch, DeepSpeed, FSDP, or Megatron-LM, with experience in distributed training optimizations.
  • Strong knowledge: LLM inference optimizations, including quantization, pruning, KV caching, continuous batching.
  • Bachelor's degree or higher: Computer Science, Engineering, or related field.
Why Join Us?

Work on cutting-edge LLM training and inference optimization at scale. Directly impact our AI infrastructure by improving efficiency and reducing costs. Collaborate with global AI/ML teams on high-impact challenges. Opportunity to research and implement state-of-the-art GPU optimizations.

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