NVIDIA
Silicon
PrincipalPerformanceandManufacturingArchitect
Neural analysis suggests this role is
optimal for Lead candidates.
“Principal Performance and Manufacturing Architect at NVIDIA. Skills: Performance Architecture, Manufacturing Architecture, Silicon Validation, First-principles Modeling. Own physics from mechanism to margin. Build first-principles models”
What You'll Achieve.
Translate directly into product outcomes at scale; Improve how the organization ships products after every program; Make it unambiguous whether a marginal defect or timing violation is detected or escapes; Every team is making decisions from the same model; Every product NVIDIA ships goes through the systems you'll help build
Industry & Context.
First-principles thinking; Engineering judgment; Turn ambiguous physical phenomena into quantified, defensible margin terms; Design the experiment that gets data
What They're Looking For.
Must Have
15+ years in the field, Deep, hands-on understanding of how transient VF behavior develops worst-case stress conditions for marginal defects and timing violations, Demonstrated experience building first-principles models connecting physical parameters to manufacturing outcomes, calibrated through real silicon, A clear track record defining manufacturing test specifications on a shipped product, with each margin term explicitly sourced and owned, Built and ran silicon validation experiments that proved models from NPI through production
Nice to Have
Applied AI to production engineering workflows — model fitting, anomaly detection, and specification generation, Worked across the VF specification and manufacturing boundary on multiple nodes, Led multi-functional alignment on a methodology disagreement, Delivered innovative solutions on programs where the schedule did not allow a second experiment
What You'll Do.
Own physics from mechanism to margin
Build first-principles models
Set screen that resolves escapes
Specify ATE and SLT conditions
Make POR the authoritative source
Author methodology document
Drive alignment across teams
Prove model before production
Own per-release validation plan
How You'll Work.
Team & Collaboration
Drive alignment across build, product definition, reliability, and test engineering; Led multi-functional alignment on a methodology disagreement
Full Job Description
NVIDIA’s Silicon Co-Design Group sits at the crossroads of architecture, silicon, systems, and manufacturing, where first-principles thinking and engineering judgment at the highest level translate directly into product outcomes at scale. We are looking for a Principal Performance and Manufacturing Architect who has built the models, defined the specs, and seen them validated through silicon. You have owned the connection between design intent and manufacturing reality, not as a reviewer or a contributor, but as the person who set the methodology and proved it worked. You turn ambiguous physical phenomena into quantified, defensible margin terms. You do not wait for data to confirm your hypothesis; you design the experiment that gets it. You improve how the organization ships products after every program. The exceptional hire also uses AI deliberately — with proven workflow impact and the judgment to know where it compresses real work and where it introduces risk. **What you 'll be doing:** * Own the physics, from mechanism to margin. Build first-principles models connecting AVF, defect mechanisms, and DVFS transients to field FIT, system-level yield, and DPPM vs. coverage — calibrated per node and population shift — so every margin term in the V/F curve and P-state table is named, sourced, and defensible. * Set the screen that resolves escapes. Specify ATE and SLT voltage, frequency, and timing conditions that capture worst-case transient VF windows — making it unambiguous whether a marginal defect or timing violation is detected or escapes at every manufacturing stage. * Make the POR the authoritative source. Author the methodology document for each program and drive alignment across build, product definition, reliability, and test engineering — so every team is making decisions from the same model. * Prove the model before production. Own the per-release validation plan — split-screen experiments, sample sizes, statistical acceptance criteria, and production moni
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