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Intel AI Software Solutions Engineer Frameworks Workloads 
India, Karnataka, Bengaluru 
278141064

20.11.2024

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Job Description
Job Description

We are looking for a senior contributor to design, develop and optimize AI frameworks for Inference. In this role, you will work with a cross-geo teams to enhance the inference stack to ensure competitive performance on deep learning inference models with a specific focus on the PyTorch framework.

The roles and responsibilities that you would need to performance may include the following:

  • Design and develop SW techniques for AI frameworks - both HW-agnostic and HW-aware
  • Contribute to enhancing and extending the Inference and Training capabilities in our Software stack
  • Profile deep learning inference workloads as needed and identify optimization opportunities
Qualifications
  • BTech, MS or PhD in CS or related fields with an overall experience of 10 to 15 years
  • Atleast 2 or 3 years of experience working on Inference frameworks/tools for inference for deep learning models and that have been deployed/used by customers
  • Architecture/Design contributions to Inference systems
  • Detailed understanding of machine learning systems optimization and deployment techniques such as quantization
  • Experience with optimization techniques for deployment of Large Language Models (LLMs)
  • Deep implementation knowledge of transformers and inference specific optimizations
  • Programming skills in Advanced C++, Python and parallel programming skills
  • Ability to debug complex issues in multi-layered SW systems
  • Understanding of SW integration across open source frameworks and internal framework layers
  • Strong understanding of computer architecture
  • Effective communication skills and experience with working in a cross-geo setup

Preferred

  • Experience working on and contributing to Inference serving solutions
  • Knowledge of compiler algorithms for heterogeneous systems
  • Knowledge of open source compiler infrastructure like LLVM or gcc
  • Understanding of low-level kernels

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