Chief AI Innovation Architect

1 week ago

Cork, Cork, Ireland beBeeMachineLearning Full time $150,000 - $175,000

Job Description:

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In this role, you will be part of a dynamic team driving innovation in Machine Learning technology. You will create and optimize critical technologies for compilers, quantization processes, ML workloads, and performance optimizations. Your work will empower the world's lowest-power ML solution, deployed in more devices globally than any other.

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Key Responsibilities:

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  • Develop and enhance ML compilers using PyTorch and C++ tailored for our Neural Processing Unit (NPU) architecture.
  • Design and implement advanced quantization techniques to improve model efficiency and accuracy.
  • Optimize ML workloads for deployment across diverse devices, ensuring top-tier performance and energy efficiency.
  • Work directly with strategic customers to address specific challenges and deliver tailored ML solutions.
  • Collaborate with architecture and software engineering teams to integrate cutting-edge ML technologies into our platforms.
  • Research, prototype, and implement innovative solutions in ML compiler design and system-level optimizations.
  • Evaluate and debug ML performance to identify and resolve bottlenecks in complex workflows.
  • Create thorough technical documentation to share knowledge and advancements with the team.
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Required Skills and Qualifications:

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We are seeking individuals with strong expertise in PyTorch and C++ programming, experience with ML workload analysis, compiler development, and quantization techniques. Familiarity with deep learning frameworks such as TensorFlow or ONNX is a plus. A proven track record of solving complex performance and efficiency challenges in hardware-aware ML solutions is essential.

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Benefits:

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You will have the opportunity to work directly with strategic customers, shape the future of energy-efficient ML technology, and contribute to impactful advancements in the field. As a member of our team, you will enjoy a collaborative and dynamic work environment that fosters growth and innovation.

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Other Opportunities:

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Contributions to ML research or publications in relevant fields are an advantage. Experience with ML model deployment on hardware accelerators such as GPUs, TPUs, or NPUs, and understanding of system-level architecture and low-level programming are also beneficial.

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