About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary Reporting to the Quality leadership within Manufacturing Operations, the Senior Reliability Scientist is responsible for leading reliability activities across complex, high-performance systems. Working closely with established reliability experts and cross-functional teams, this role uses experimental data and advanced modelling to inform design decisions, validate product reliability and optimise serviceability strategies, including spares provisioning. The Team The Quality team within Manufacturing Operations is responsible for ensuring product robustness, reliability and lifecycle performance across Graphcore’s hardware portfolio. The team includes experienced reliability specialists and works closely with technology research, chip, board, system design, platform and operations teams to translate reliability insights into actionable improvements across the product lifecycle. Responsibilities and Duties: · Define and refine reliability requirements across silicon, board and system levels, working in partnership with research and design teams · Apply ad
Principal Performance and Manufacturing Architect
Salary not disclosed
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Role overview
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 monitoring — through QS sign-off.
What we need to see:
BSEE / MSEE / PhD or equivalent experience, with 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 — you know the mechanisms, not just the models.
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, not as a supporting contributor, but as the person who developed and was responsible for the experiments.
Ways to stand out from the crowd:
You have applied AI to production engineering workflows — model fitting, anomaly detection, and specification generation — and can describe the outcomes and the guardrails you put in place.
Worked across the VF specification and manufacturing boundary on multiple nodes and can articulate how your approach evolved as defect populations shifted.
Led multi-functional alignment on a methodology disagreement and brought the organization to a defensible, shared decision.
Delivered innovative solutions on programs where the schedule did not allow a second experiment.
The payoff is that every product NVIDIA ships goes through the systems you'll help build. If that's the kind of problem you want to work on, we'd like to talk! NVIDIA is widely considered one of the technology world’s most desirable employers — home to some of the most forward-thinking engineers in the industry. If you build models others depend on, set specifications that hold up through production, and make the next program better because of what you learned, we want to hear from you.
#LI-Hybrid
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.What they are looking for
Skills & requirements
Qualification
What we need to see: BSEE / MSEE / PhD or equivalent experience, with 15+ years in the field
Hiring company
Nvidia
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