Senior Deep Learning Software Engineer

Invidia

Multiple Locations
Base: 224,000 usd - 356,500 usd; bonus/equity: eli...
Hybrid
Deep learning software design
Python and pytorch proficiency
Gpu kernel development
You will play a pivotal role in defining a modular, scalable platform to seamlessly bridge training and deployment workflows enabling tight integration with training frameworks such as Megatron and Nemo

Job Summary

  • You will play a pivotal role in defining a modular, scalable platform to seamlessly bridge training and deployment workflows enabling tight integration with training frameworks such as Megatron and Nemo.
  • Your work will span multiple layers of the DL deployment stack including high-level framework development, GPU kernel optimizations, and performance profiling to maintain NVIDIA's leadership in inference software solutions.
  • This is an exceptional opportunity to join NVIDIA’s model optimization group and help build real-time, cost-effective computing platforms in a rapidly growing field.

Matching Summary

You will play a pivotal role in defining a modular, scalable platform to seamlessly bridge training and deployment workflows enabling tight integration with training frameworks such as Megatron and Nemo.

Salary

Base: 224,000 USD - 356,500 USD; Bonus/Equity: Eligible for equity; Benefits: Eligible for benefits

Skills & Requirements

Must-have

  • Deep Learning software design
  • Python and PyTorch proficiency
  • GPU kernel development
  • Inference optimization techniques
  • Performance analysis and debugging
  • High-performance execution environment

Nice-to-have

  • Contributions to ML frameworks
  • Knowledge of GPU architecture
  • Familiarity with NVIDIA SDKs
  • Collaborative and independent work
  • Fast-paced environment adaptability

Key Requirements

  • Masters, PhD, or equivalent experience
  • 8+ years deep learning experience
  • Strong software design skills
  • Good communication skills
  • Ability to work independently and collaboratively

Work Rights

Not specified

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