Sr Deep Learning Hardware Engineer

Intel Intel · Semiconductors · Leixlip, Ireland

The Sr Deep Learning Hardware Engineer role focuses on the pre-silicon validation of NPU AI accelerator products. Responsibilities include functional, power, and performance validation of complex digital designs, architecting and implementing verification environments, and developing test plans and cases. The role requires collaboration with design engineers, model engineers, and AI architects to validate components of next-generation NPU IP portfolios and influence the AI product roadmap. Experience with deep learning techniques and frameworks is considered an advantage.

What you'd actually do

  1. Functional and/or Formal Verification of complex low power digital design block(s) on the latest generations of Intel's NPU AI accelerators.
  2. Architect, development and implementation of verification environments from initial planning through the validation lifecycle to review and signoff.
  3. Development of test plans and test cases
  4. Implementation of random test generators, high level transactional models, bus functional models (BFMs), functional/formal constraints, checkers and scoreboards, coverpoints/covergroups and SVA properties
  5. Collaborate with cross-functional teams to analyses and address next-generation AI requirements, influencing the AI product roadmap.

Skills

Required

  • 8+ years of relevant industry experience in pre-silicon validation of IP's, ASIC, SoC, or FPGA designs
  • Fully proficient with industry standard verification tools and methodologies (System Verilog, VCS, UVM, OVM etc)
  • Expertise in developing testbenches, test plans, and functional/formal verification environments
  • Proven track record of signing off complex designs through coverage closure and related techniques
  • Strong problem-solving skills

Nice to have

  • Experience with validating low power and high performance designs, HW accelerators being a major plus
  • Proficiency in System Verilog Assertions
  • Experience with formal verification techniques and tools
  • Knowledge of Git and Continuous Integration (CI) practices
  • Established knowledge of SoC based CPUs, NoCs, AMBA protocols, and memory controllers
  • Experience with other programming languages such as C, C++, Python, Shell scripting and TCL
  • Understanding of deep learning techniques and experience working with frameworks such as TensorFlow, PyTorch, or OpenCV is an advantage
  • Passion for exploring and AI techniques, tools and flows to aid in faster validation closure

What the JD emphasized

  • pre-Silicon validation
  • pre-Silicon verification
  • AI accelerator

Other signals

  • NPU AI accelerator products
  • AI technologies
  • AI product roadmap
  • AI hardware development
  • AI models
  • AI workloads