below marketmost similar roles pay hereabove market
This role pays more than
65%
of similar roles. Most pay
$158,325–$236,662
— the blue band above.
At the midpoint, this role pays about
$216k
versus about
$197k
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
1792 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1179 roles with salary data.
Modeling Engineer, Supply Chain Optimization joins the team to own and extend a production linear programming optimization model. This role involves developing constraint matrices, objective functions, and dual-variable extraction logic to manage complex hardware supply chains involving wafers, assembly methods, and interconnect materials. You will translate physical supply chain realities like yields, lead times, and capacity limits into mathematical formulations. The work focuses on identifying constraints and allocating capacity across multi-quarter planning horizons to improve business opportunities while managing supply risk. Required skills include proficiency in MATLAB, Julia, R, Python, and solvers like Gurobi or CPLEX. You must possess deep knowledge of duality, shadow prices, and sensitivity analysis. The role serves as a quantitative thought partner to leadership, using optimization to solve high-level sourcing and procurement problems.
Own and extend a production linear programming optimization model for supply chain planning.
Develop constraint matrices, objective functions, and dual-variable extraction logic for the optimization engine.
Translate physical supply chain realities like wafer nodes and lead times into mathematical formulations.
Analyze shadow prices and sensitivities to provide actionable insights for executive planning and procurement.
Maintain the integrity of model inputs, data lineage, schemas, and downstream dependencies.
Partner with cross-functional teams to quantify constraints and develop scenarios for resource management.
Serve as a quantitative thought partner to senior leadership to evolve the model architecture.
Explore augmenting deterministic optimization with AI, machine learning, and GPU-accelerated technologies like cuOpt.
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 a higher education, modeling, engineering, or data science position.
Strong hands-on experience formulating and solving linear programming or mixed-integer programming problems, including developing objective functions and constraints.
Deep understanding of constrained optimization foundations, including duality, shadow prices, sensitivity analysis, degeneracy, numerical conditioning, and solver behavior.
Proficiency in scientific computing and mathematical optimization techniques using MATLAB, Julia, R, Python, or comparable environments.
Understanding of supply chain modeling concepts such as bills of materials, capacity constraints, lead times, yields, and multi-period planning.
Ability to translate complex physical or organizational systems into rigorous mathematical formulations and explain results to analytical and operational collaborators.
Advanced research or publications in operations research or mathematical optimization (preferred).