We are hiring a Senior Manager to lead our Silicon Speed Productization team. In this role you will own simulation-to-silicon speed correlation and determine whether each product delivers its intended frequency across voltage, process, temperature, workload, and lifetime conditions. This is not a coordination role or a validation-only role. You and your team will identify where predictions diverge from measured silicon, drive the hardest issues to root-cause closure, and establish the technical basis for product limits, guardbands, binning, and production sign-off!
Your organization will lead senior individual contributors and a first-line manager at the boundary between what was designed and what was built. Architecture, design, product, and senior leaders will use your data and judgment to make program decisions. Success in this role means that speed risks are raised early, correlation improves with every generation, and programs ship at speed-of-light schedules. The exceptional leader also applies AI deliberately-with demonstrated workflow impact!
What you'll be doing:- Close the prediction-to-silicon loop: Own speed characterization and correlation from pre-silicon planning and first power-on through production sign-off across GPU, CPU, and SoC programs
- Resolve the hardest speed failures: Drive root-cause closure when a frequency corner does not hold, minimum operating voltage is too high, a critical path escapes timing analysis, or workload behavior diverges from the model
- Define product limits with evidence: Refine methodologies for silicon margining, process-voltage-temperature characterization, guardbanding, aging, and speed binning, and translate the results into product requirements and production decisions
- Left-shift learning: Feed measured-silicon insights back into timing models, critical-path predictions, test content, design practices, and future architectures so that each program begins with a stronger correlation baseline
- Give leadership the clarity to act: Convert characterization data, model deltas, yield trends, and program risks into crisp, decision-ready options for leadership on plan-of-record, qualification, production, and ramp decisions
- Scale analysis and coverage: Build closed-loop data, automation, and AI-assisted workflows for test orchestration, anomaly detection, triage, and reporting that improve coverage and cycle time
- Build the organization NVIDIA depends on: Hire, develop, and retain a high-performing, globally-distributed team; grow engineers into recognized subsystem experts and develop first-line managers into independent leaders
What we need to see:- BS, MS, or PhD in Electrical Engineering, Computer Engineering, Systems Engineering (or equivalent experience)
- 12+ overall years of experience in silicon characterization, post-silicon validation, timing or performance analysis, including 5+ years leading technical teams
- Proven management experience developing senior engineers and first-line managers
- Deep technical foundation in digital design, computer architecture, static timing analysis, critical-path behavior, clocking, power and timing interactions, semiconductor process variation, sampling, and statistics.
- Hands-on expertise with silicon speed characterization, simulation-to-silicon correlation, voltage-frequency margining, guardbanding, and process-voltage-temperature and binning dependencies for high-performance silicon
- Demonstrated ability to drive ambiguous, system-level issues to root-cause closure across the company
- Proficiency with data-analysis and automation tools such as Python, JMP, SQL, or equivalent, with the judgment to distinguish correlation from causation in large silicon datasets
- Strong written and verbal communication; able to translate complex technical issues into decision-ready options for leadership
Ways to stand out from the crowd:- Evidence that you traced a frequency miss to a specific critical path, microarchitectural interaction, clocking condition, or process corner and drove a productized fix through confirmation.
- Experience building reusable correlation, margining, or characterization methodologies adopted across silicon programs or advanced process nodes.
- Applied AI tools to characterization or debug workflows to lift team velocity
- Comfort navigating ambiguity, setting technical direction at speed-of-light schedules, and aligning global teams around a clear plan of record.
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 232,000 USD - 368,000 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
