Member of Technical Staff (GPU Performance Engineer)

Reka

• $150K — $180K *
US-AnywhereRemote in United States
Consumer Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Strong engineering skills with Python and PyTorch (or similar frameworks)
  • Proven experience with large deep learning models
  • Experience with low-level GPU code (CUDA, C++)
  • Experience scaling GPU jobs on large compute clusters (e.g., Slurm, Kubernetes)
  • Ability to analyze and tune performance of GPU workloads

Responsibilities

  • Design and implement improvements to training infrastructure
  • Contribute to technical decisions optimizing model performance
  • Work on post-training processes like reinforcement learning
  • Fine-tune models for improved effectiveness
  • Enhance efficiency and scalability of model serving infrastructure

Benefits

  • Collaborative work environment with skilled professionals
  • Opportunity to influence technical decisions
  • Access to cutting-edge technology and frameworks
  • Potential for professional development and growth
  • Flexible work arrangements to support work-life balance
Full Job Description
We are seeking an experienced GPU Performance Engineer with a strong background in Python and large-scale model training. In this role, you will design and implement improvements to our training infrastructure and directly contribute to technical decisions that optimize performance of our models. You will also work on post-training processes, including reinforcement learning and fine-tuning. Furthermore, you will contribute to improving the efficiency and scalability of our model serving infrastructure.

Ideal Experience
  • Strong engineering skills with fluency in Python and PyTorch (or other frameworks).
  • Proven experience implementing and training large deep learning models.
  • Experience writing and debugging low-level GPU code (CUDA, C++).
  • Experience scaling up GPU jobs using large-scale compute clusters (e.g., Slurm or Kubernetes).
  • Demonstrated ability to analyze and optimize the performance of GPU-accelerated workloads, including profiling, identifying bottlenecks, and implementing performance tuning techniques.

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