GPU Optimization Specialist

2 weeks ago

Singapore beBeeEngineer Full time $150,000 - $200,000
Job Summary

We are seeking a highly skilled GPU Training & Inference Optimization Engineer to join our team. As a key member of our AI/ML group, you will be responsible for optimizing the performance, efficiency, and scalability of large language model (LLM) training and inference workloads on our GPU clusters.

Your expertise in GPU-accelerated ML frameworks, distributed training, and inference optimization will enable us to improve the efficiency and reduce costs of our AI infrastructure. You will work closely with global AI/ML teams to tackle high-impact challenges and implement state-of-the-art GPU optimizations.

About the Role

This role requires strong expertise in PyTorch, DeepSpeed, FSDP, or Megatron-LM, with experience in distributed training optimizations. You should have a deep understanding of LLM inference optimizations, including quantization, pruning, KV caching, and continuous batching.

You will be responsible for:

  • Optimizing LLM training frameworks to maximize GPU utilization and reduce training time.
  • Profiling and optimizing distributed training bottlenecks.
  • Implementing and tuning inference optimizations for low-latency, high-throughput LLM serving.

You will collaborate with infrastructure teams to improve GPU cluster scheduling, resource allocation, and fault tolerance for large-scale training jobs.

Key Responsibilities

Develop benchmarking tools to measure and improve training throughput, memory efficiency, and inference latency.

Research and apply cutting-edge techniques to optimize LLM performance.

Mandatory Qualifications

3+ years of hands-on experience in GPU-accelerated ML training & inference optimization, preferably for LLMs or large-scale deep learning models.

Deep expertise in PyTorch, DeepSpeed, FSDP, or Megatron-LM, with experience in distributed training optimizations.

Strong knowledge of LLM inference optimizations, including quantization, pruning, KV caching, and continuous batching.

Nice-to-Have Skills

Proficiency in CUDA, Triton kernel, NVIDIA tools, and performance profiling.

Experience with LLM-specific optimizations, including FlashAttention, PagedAttention, LoRA, and speculative decoding.

Familiarity with Kubernetes for GPU workloads.

Contributions to open-source ML frameworks.

Benefits

Work on cutting-edge LLM training & 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.

Rakuten is an equal opportunities employer and welcomes applications from all qualified candidates.

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