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Head of AI Embedded Software – NPU Platform

Nuvoton

Where
Herzliya, Israel, hybrid
Language, from the listing
No Hebrew mentionedA missing mention doesn't mean Hebrew isn't needed. Ask if it matters to you.
Dates
Found 1 Oct 2026
Last checked on the employer's site
1 h ago (6 Oct 2026)
Source
Employer career page (Comeet)

What they ask for

  • MSc or PhD in Computer Science, Electrical Engineering, or equivalent
  • Proven track record leading engineering team delivering complex software platforms
  • Deep understanding of how machine learning models are mapped to hardware accelerators (NPUs, GPUs, DSPs, or FPGAs)
  • Deep understanding of how AI models (CNNs, Transformers, RNNs) are mapped onto hardware accelerators (NPUs, DSPs, or FPGAs)
  • Hands-on experience with AI compiler stacks: MLIR, LLVM, TVM, XLA, or similar
  • Solid background in systems software, including runtime environments, drivers, and performance-critical software
  • Experience building up a group of top-tier talent in the competitive AI space
  • Customer-facing experience with a partnership mindset

Nice to have

  • 10+ years in software development for complex systems (semiconductor or AI infrastructure preferred)
  • Nice to Have: Data center or cloud computing background, ML deployment frameworks, or SDK/developer tools experience.

The full listing

Description Nuvoton Technology Israel is a leading semiconductor design center, developing SoCs, microcontrollers, and security hardware solutions for tier-1 customers in the computing and server space. As a self-contained R&D center — spanning architecture, chip design, software, and system engineering — we work closely with major US-based OEMs to deliver innovative, semi-custom silicon solutions. We are now extending our portfolio into AI acceleration, developing a next-generation NPU platform aimed at efficient, real-world AI deployment. Shape the Future of AI at the Silicon Level We are looking for a visionary Senior Technical Manager to build and lead our AI software stack for our next-generation NPU platform. This is a rare opportunity to own the full software layer — from compiler infrastructure to developer ecosystem — and make a lasting impact on how AI runs on silicon. What You'll Do; • Build and lead a highly skilled AI software & algorithms engineering team, establishing engineering standards, development processes, and technical direction • Define the end-to-end stack — compiler, runtime, kernel libraries, and SDK — enabling efficient AI deployment on our NPU • Drive AI compiler development using technologies like MLIR, TVM, LLVM or similar infrastructures to translate PyTorch/TensorFlow models into optimized NPU execution • Champion model optimization — quantization, pruning, and hardware-aware techniques for maximum performance and power efficiency on the accelerator. • Runtime, Drivers, and Firmware Integration — scheduling, memory management, and low-level software for AI workloads on-chip • AI Kernel Libraries - Guide the development of highly optimized neural network kernel libraries and performance-critical primitives tailored to the architecture of the NPU. • Developer Ecosystem - Define and deliver the SDK, APIs, and development tools that allow internal teams and external developers to deploy AI models easily on the platform. • Cross-Functional Architecture Collaboration - Work closely with silicon architecture and hardware design teams to ensure optimal hardware-software co-design, providing feedback on architecture, performance bottlenecks, and future ISA requirements. Requirements • MSc or PhD in Computer Science, Electrical Engineering, or equivalent • 10+ years in software development for complex systems (semiconductor or AI infrastructure preferred) • Proven track record leading engineering team delivering complex software platforms • Deep understanding of how machine learning models are mapped to hardware accelerators (NPUs, GPUs, DSPs, or FPGAs) • Deep understanding of how AI models (CNNs, Transformers, RNNs) are mapped onto hardware accelerators (NPUs, DSPs, or FPGAs) • Hands-on experience with AI compiler stacks: MLIR, LLVM, TVM, XLA, or similar • Solid background in systems software, including runtime environments, drivers, and performance-critical software • Experience building up a group of top-tier talent in the competitive AI space • Customer-facing experience with a partnership mindset Nice to Have: Data center or cloud computing background, ML deployment frameworks, or SDK/developer tools experience.