EnCharge AI is looking for an exceptional technical leader who can envision, build, and lead algorithm-hardware codesign efforts for the next generation of AI Inference Hardware. You must have a strong track record of AI algorithm-hardware codesign innovations as well as providing technical leadership to teams in AI Algorithms & Hardware codesign space. This position is a strong growth opportunity, with the possibility of transitioning into further leadership roles in the future.

  • Enabling quantization-aware training (QAT) and post-training quantization (PTQ) techniques/packages that work with open-source frameworks, including Tensorflow (TF) and/or PyTorch (PT).
  • Driving key innovations that enable quantized AI models to work without loss of accuracy on EnCharge hardware.
  • Building Python-based SDKs that enable fully automated quantization schemes for AI models.
  • Work with software teams to build and enable AI services & SDKs that exploit Neural Architecture Search (NAS) for model builds – with the goal of designing and discovering EnCharge HW optimized models for customer-specific datasets.
  • Drive AI algorithms-hardware roadmap features, project management and schedules.
  • Working with clients to understand the specifics of their AI models and to translate those requirements to EnCharge teams working in algorithms, hardware, and software.
  • Mentor/lead junior engineers across the company.

Qualifications/Required Skills:

  • Masters / Ph.D. in EE/CS with >5 years of industry experience in AI algorithms & hardware.
  • At least 3-5 years of experience with C++, Python, Tensorflow & PyTorch and >1-2 years of experience in the code structure (and modifying operators) in Tensorflow & PyTorch.
  • > 1-2 years of experience with Cuda.
  • Deep experience with state-of-the-art neural network topologies in various application domains.
  • Knowledge and implementation of advanced QAT and PTQ techniques used to build highly quantized AI models.
  • 3-5 years of experience with algorithms-hardware codesign.
  • Excellent verbal and communication skills. 
  • Client engagement experience.

Preferred/Beneficial Skills:

  • Knowledge of the end-to-end runtime stack for AI applications including TVM.
  • Knowledge of AI Hardware libraries and compiler stacks (including LLVM and MLIR).

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