Deep Learning System Validation Engineer

Intel Intel · Semiconductors · Haifa, Israel

This role focuses on validating and developing hardware structures and interfaces to accelerate deep learning hardware and software performance for AI systems. It involves developing test plans, leading pre- and post-silicon activities, and collaborating with product and design teams to define next-generation requirements and influence the AI product roadmap. The work impacts AI solutions in both on-device and data center deployments.

What you'd actually do

  1. Develop and specify deep learning hardware test plans and interfaces that enhance AI systems performance.
  2. Lead pre- and post-silicon activities, including test flows, interface compliance, and analysis on AI platforms.
  3. Collaborate with product and design teams to define next-generation requirements and explore opportunities for innovation in AI system hardware.
  4. Engage with customer-facing teams to analyze and validate deep learning product performance and power data.

Skills

Required

  • Bachelor's degree in Electrical Engineering, Computer Engineering, or a related field
  • 7+ years of experience in board/system validation or board design
  • Proficiency in board design and systems validation
  • Strong problem-solving skills and systems engineering fundamentals
  • Advanced experience in developing test plans
  • lab hands on with high speed interfaces or power measurements

Nice to have

  • Familiarity with high speed interfaces' validation techniques
  • Demonstrated ability to work closely with cross-functional teams, including customer-facing and design teams.
  • Strong communication skills and ability to influence and guide product development in complex technical domains.

What the JD emphasized

  • advanced hardware structures
  • deep learning hardware
  • AI systems performance
  • AI product roadmap
  • model efficiency
  • architecture
  • AI solutions deployed on device and in the data center
  • artificial intelligence and machine learning technologies
  • deep learning product performance and power data

Other signals

  • deep learning hardware
  • AI systems performance
  • AI product roadmap
  • AI solutions deployed on device and in the data center