Silicon Validation Engineer (RDSS Intern)

Benefits

• and cost of architected features post-silicon, and provide the data that determines what gets built into future products. • Build methodologies that last.
• Develop characterization and validation methodologies that close the simulation-to-silicon gap, correlate measured behavior to design, and scale across products and generations. • Drive failure analysis.
• Form hypotheses, design experiments, trace root causes across circuit, firmware, and software layers, and don't stop until the answer is confirmed. • Analyze real-world workloads — LLM inference and training, generative AI, AAA games, autonomous driving stacks — to push best-in-class performance and energy efficiency. • Build and deploy AI-driven analysis flows, intelligent data pipelines, and automated triage tools that expand coverage and compress cycle time. • Build tools the team runs on — automation for characterization, data collection, test execution, and analysis.
• What you build will be used.
• What we need to see: • BS, MS, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or Systems Engineering. • Solid fundamentals in digital design, computer architecture, circuit analysis, signal integrity, and statistics — the kind you can apply under pressure, not just in coursework. • Proficiency in Python, C/C++, or equivalent.
• You build things.
• You don't wait for a tool to exist. • The instinct to own outcomes.
• When something is wrong, you notice.
• When you notice, you act. • You don't need to check every box.
• We care more about how you think, how you learn, and what you've built than whether your background maps perfectly to this list.
• If this role excites you, apply.
• Ways to stand out from the crowd: • You've built something with AI, LLMs, or agentic workflows that solved a real engineering problem. • You've developed a methodology or framework that someone else adopted — your work became the standard, not just the solution. • You've evaluated a feature, weighed benefit against cost, and produced a recommendation that influenced a real decision. • Lab experience with oscilloscopes, logic analyzers, BERTs, or similar.
• The chips your team works on power the world's most advanced GPUs, workstations, datacenter AI infrastructure, and autonomous vehicles.
• The engineers who built them started exactly where you are.
• The difference is they chose to start here — where the ownership is real, the methodologies you build shape future programs, and the features you define ship to the world.
• If that's where you want to begin, we want to hear from you.