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Senior AI Software Engineer

European Tech Recruit
Cambridge
10 months ago
Applications closed

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Required Qualifications:

  • Extensive experience in optimising AI chip architectures and AI systems, with deep familiarity with mainstream heterogeneous computing software and hardware architectures. Comprehensive expertise spanning applications, foundational software, and chip design.
  • Hands-on experience in at least one of the following areas: numerical computation, compilation, algorithm and chip co-design, runtime systems, or shared memory management.
  • Solid understanding of AI industry application scenarios, mainstream models, and algorithm development trends, with the ability to derive chip-layer requirements from these insights.
  • Expertise in analysing workload sensitivity to micro-architecture features, evaluating performance trade-offs, and providing recommendations to optimise both micro-architecture and application software.
  • Familiarity with the performance impact of various compute, memory, and communication configurations, as well as hardware and software implementation choices for AI acceleration.
  • Proficiency with GPU compute APIs like CUDA or OpenCL, and experience leveraging GPU/NPU-optimised libraries to enhance performance.
  • Practical experience in developing deep learning frameworks, compilers, or system software.
  • Strong background in compiler optimisation techniques; familiarity with LLVM-MLIR is a plus.
  • Proficiency in software development using C/C++ and Python.


Desired Qualifications:

  • Relevant experience in multiple subfields of AI, including application algorithms, frameworks, runtime systems, modelling and simulation, and compilers.
  • In-depth understanding of innovative methods, platforms, and tools used by leading AI manufacturers, with proven experience in translating academic or research achievements into commercial products.
  • Experience with GPU acceleration using AMD or NVIDIA GPUs.
  • Expertise in developing inference backends and compilers for GPU or NPU systems.
  • Proficiency with AI/ML inference frameworks such as ONNXRuntime, IREE, or TVM.
  • Practical experience deploying AI models in production environments.

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