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Head of Engineering [Riyadh - Hardware - GPU]

We’re partnering with a fast-scaling deep tech startup building next-generation AI infrastructure, combining cutting-edge hardware and software to deliver high-performance, on-premise AI compute solutions.


Backed by leading enterprise customers and operating in one of the most important technology categories of the next decade, this company is now looking for a Head of Engineering to build and lead its engineering function from the ground up.


The Opportunity

This is a rare chance to step into a foundational leadership role at an early stage and shape the entire engineering organisation.


You’ll be taking ownership of both hardware and software engineering, while remaining hands-on technically. This isn't a pure management role; you’ll be expected to contribute directly to architecture, code, and system design. Over time, you’ll scale the team, introduce structure, and build a high-performing engineering culture across multiple disciplines.


The Role

  • Own engineering across hardware + software
  • Stay hands-on (coding, architecture, system design)
  • Scale and structure the team from the ground up
  • Drive development across infrastructure, low-level systems, and performance optimisation


What They’re Looking For

  • Proven experience leading multiple teams or managers in complex engineering environments
  • Strong hands-on technical background.
  • Full-stack systems exposure, from high-level applications to low-level systems
  • Experience working in Linux-based environments and infrastructure platforms
  • Proficiency in at least one: Python, C++, C, or Node.js
  • Background in compute platforms, distributed systems, or GPU-related infrastructure
  • Experience in hardware-adjacent environments (e.g. embedded systems, consoles, SDKs, cloud infrastructure)
  • Genuine passion for systems, performance, and hardware


Nice to Have

  • Experience with GPU acceleration, CUDA, or low-level performance optimisation
  • Exposure to ML systems, inference engines, or model training workflows
  • Knowledge of hardware design, cooling systems, or physical system constraints
  • Experience with manufacturing, supply chain, or system assembly
  • Strong product mindset with an eye for performance and design

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