Neuromorphic AI Kernel Engineer - Hybrid

Intel Corporation

Santa Clara (CA)

Hybrid

USD 171,000 - 241,000

Full time

2 days ago
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Job summary

Intel Corporation's Neuromorphic Computing Lab is seeking an AI Software Development Engineer to translate neuromorphic hardware features into high-performance AI software. You will develop kernels and programming abstractions for Loihi-based systems, collaborating with hardware architects, compiler engineers, and AI researchers.

You will optimize workloads such as convolutions, transformers, and generative AI, build performance models and reference implementations, and contribute to end-to-end

Qualifications

  • Minimum of a PhD with no professional experience, or a master's degree with 2+ years of relevant experience, or a bachelor's with 4+ years of relevant experience.
  • 4+ years of software development experience including Python and C/C++, with performance-critical code.
  • 2+ years of kernel development using CUDA/SYCL/OpenCL or DSLs.

Responsibilities

  • Design and implement specialized AI kernels for neuromorphic hardware using CUDA/SYCL or DSLs.
  • Optimize convolutions, transformers, and generative AI workloads.
  • Develop performance models, benchmarks, and validation tools for hardware-software co-design.
  • Collaborate across hardware, compiler, runtime, and application teams to deliver maintainable code and tests.

Skills

Python
C/C++
Parallel programming
Performance optimization
PyTorch/JAX/TF

Education

Bachelor's degree in CS/EE/CE
Master's degree
PhD

Tools

CUDA
SYCL
OpenCL
MLIR
Triton

Job description

Intel Corporation's Neuromorphic Computing Lab is seeking an AI Software Development Engineer to translate neuromorphic hardware features into high-performance AI software. You will develop kernels and programming abstractions for Loihi-based systems, collaborating with hardware architects, compiler engineers, and AI researchers.

You will optimize workloads such as convolutions, transformers, and generative AI, build performance models and reference implementations, and contribute to end-to-end

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