NVIDIA
Modeling Engineer, Supply Chain Optimization
$168K–$322K
US, CA, Santa Clara · Full-time · Posted Sep 15, 2026
- Experience
- A minimum of 8 years; no alternatives stated.
- Education
- Master’s degree or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Management Science, or a closely related quantitative subject area, or equivalent experience.
About the job
Own and extend a production linear programming model that plans constrained supply across NVIDIA’s hardware supply chain and informs sourcing, procurement, and capacity decisions.
What you'll do
- Develop and maintain optimization formulations, including constraints, objective functions, dual-variable logic, and diagnostics.
- Translate supply-chain conditions into model inputs and analyze sensitivities and shadow prices to guide planning decisions.
- Partner with supply-chain and business teams to validate assumptions, develop scenarios, and improve model architecture.
What they ask for
- Master’s degree or PhD in a related quantitative field, or equivalent experience.
- At least 8 years of experience in a higher education, modeling, engineering, or data science position.
- Hands-on experience formulating and solving LP or MIP problems, including developing objectives and constraints and interpreting dual variables.
- Strong understanding of constrained optimization, plus scientific computing and experience implementing optimization models in a quantitative computing environment.
Nice to have
- Research or publications in operations research, mathematical optimization, or supply-chain optimization.
- Experience with LP/MIP solvers, manufacturing or semiconductor supply-chain data, or NVIDIA cuOpt and GPU-accelerated optimization.
Pay
168,000 USD - 264,500 USD base salary (Level 4); 200,000 USD - 322,000 USD base salary (Level 5)
Tools
MATLAB, Julia, R, Python, Gurobi, CPLEX, NVIDIA cuOpt
AI focus
Explore augmenting deterministic optimization with AI, machine learning, GPU-accelerated optimization, and NVIDIA technologies such as cuOpt.
Model-written summary · the original posting is authoritative