Quantitative Optimization Modeler

NVIDIA is seeking a quantitative optimization modeler to own and extend a production linear programming optimization model. The model determines supply needs, identifies constraints, and allocates capacity across a multi-quarter planning horizon to improve business opportunity while managing supply risk.

Responsibilities:

• Own and extend a production linear programming optimization model
• Translate evolving physical supply chain realities into mathematical formulations
• Analyze shadow prices, sensitivities, and other LP diagnostics to identify the economic impact of supply constraints
• Maintain the integrity of model inputs and assumptions
• Partner with supply chain, procurement, operations, and engineering teams to identify high-value planning decisions
• Serve as a quantitative thought partner to senior supply chain leadership
• Explore opportunities to augment the deterministic optimization foundation with AI, machine learning, and NVIDIA technologies

Requirements:

• Master's degree or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Management Science, or a closely related quantitative subject area
• 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
• Deep understanding of constrained optimization and the mathematical foundations underlying LP/MIP
• Strong scientific computing skills combined with proficiency in mathematical optimization techniques
• Understanding of supply chain modeling concepts
• Ability to translate complex physical or organizational systems into rigorous mathematical formulations

Nice to Have:

• Advanced research or publications in operations research, mathematical optimization, supply chain optimization, prioritization, or related fields
• Deep experience with LP/MIP solvers and mathematical programming environments
• Experience developing optimization models using real-world manufacturing or supply chain data
• Familiarity with NVIDIA cuOpt, GPU-accelerated optimization, or techniques that combine deterministic optimization with AI or machine learning