This role pays more than
81%
of similar roles. Most pay
$142,000–$200,625
— the shaded band above.
At the midpoint, this role pays about
$216k
versus about
$171k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
910 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 894 roles with salary data.
The Modeling Engineer, Supply Chain Optimization joins a team developing and operating a proprietary mathematical optimization model to manage complex hardware supply chains for AI infrastructure. This role involves owning and extending production linear programming models by developing constraint matrices, objective functions, and dual-variable extraction logic. The engineer will translate physical supply chain realities—such as wafer nodes, packaging architectures, and lead times—into mathematical formulations while analyzing shadow prices and sensitivities to provide actionable insights for executive planning. Candidates must possess a master's degree or PhD in a quantitative field and have extensive experience with linear programming, mixed-integer programming, and scientific computing in MATLAB, Julia, R, or Python. The role requires expertise in translating complex systems into rigorous formulations while exploring advancements in GPU-accelerated optimization and technologies like cuOpt to solve capacity constraints across multi-period planning horizons.
Own and extend a production linear programming optimization model including constraint matrices and objective functions.
Translate physical supply chain realities like wafer nodes and lead times into mathematical formulations for the optimization engine.
Analyze shadow prices and other LP diagnostics to identify economic impacts of supply constraints.
Convert complex model results into actionable insights for executive planning, procurement, and supplier discussions.
Maintain the integrity of model inputs by managing data lineage, schemas, and downstream dependencies.
Partner with cross-functional teams to stress-test assumptions and develop scenarios that improve resource management.
Serve as a quantitative thought partner to senior leadership to evolve the optimization architecture.
Explore opportunities to integrate AI, machine learning, and GPU-accelerated optimization into the existing model.
What we're looking for
Master’s degree or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Management Science, or a related quantitative field (or equivalent experience).
Minimum of 8 years of experience in higher education, modeling, engineering, or data science.
Strong hands-on experience formulating and solving linear programming or mixed-integer programming problems.
Deep understanding of constrained optimization mathematical foundations, including duality, shadow prices, sensitivity analysis, and solver behavior.
Proficiency in scientific computing using MATLAB, Julia, R, Python, or comparable quantitative environments.
Understanding of supply chain modeling concepts such as bills of materials, capacity constraints, lead times, and multi-period planning.
Ability to translate complex physical systems into rigorous mathematical formulations for both analytical and operational collaborators.
Advanced research/publications in optimization (preferred); experience with Gurobi or CPLEX (preferred); familiarity with semiconductor supply chains (preferred).