Modeling Engineer, Supply Chain Optimization

NVIDIA•Us, CA

About The Position

NVIDIA is at the center of the AI infrastructure revolution, building the accelerated computing systems that power some of the planet’s most advanced AI factories. Behind those systems is one of the most complex hardware supply chains in the industry. It includes wafers, sophisticated assembly methods, interconnect materials, printed circuit boards, power components, and other capacity-constrained technologies. Our team is developing and operating a proprietary mathematical optimization model that helps determine how much supply NVIDIA needs. It identifies constraints and allocates unusual capacity across a multi-quarter planning horizon to improve business opportunity while managing supply risk. Built on linear programming and dual-variable analysis, the model transforms complex supply constraints into clear, auditable insights. These insights can advise high-level planning, sourcing, vendor coordination, purchasing, and supplier capacity decisions. We are looking for a deeply quantitative optimization modeler who can take ownership of this production model, extend its mathematical capabilities, and ultimately become the technical authority for its optimization architecture. This is an opportunity for someone who sees supply chain planning fundamentally as a mathematical modeling problem and wants their work to directly influence consequential decisions at the frontier of AI infrastructure.

Requirements

Master’s degree or PhD in the field of Operations Research, Industrial Engineering, Applied Mathematics, Management Science, or a closely related quantitative subject area, or equivalent experience.
A 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 direct experience developing objective functions and constraints and extracting and interpreting dual variables.
Deep understanding of constrained optimization and the mathematical foundations underlying LP/MIP, including duality, shadow prices, sensitivity analysis, degeneracy, numerical conditioning, and solver behavior.
Strong scientific computing skills combined with proficiency in mathematical optimization techniques, with experience implementing production or research optimization models in MATLAB, Julia, R, Python, or comparable quantitative computing environments.
Understanding of supply chain modeling concepts such as bills of materials, capacity constraints, lead times, yields, allocation decisions, and multi-period planning.
Ability to translate complex physical or organizational systems into rigorous mathematical formulations and explain model assumptions, behavior, tradeoffs, and results to both analytical and operational collaborators.
Demonstrated ability to operate as a highly hands-on individual contributor, taking end-to-end ownership of complex quantitative work while collaborating effectively with senior engineering and commercial partners.

Nice To Haves

Advanced research or publications in operations research, mathematical optimization, supply chain optimization, prioritization, or related fields, including work presented through INFORMS, IISE, or peer-reviewed journals.
Deep experience with LP/MIP solvers and mathematical programming environments such as MATLAB Optimization Toolbox, Gurobi, CPLEX, or similar technologies, including interpretation of dual variables and solver diagnostics beyond basic model execution.
Experience developing optimization models using real-world manufacturing or supply chain data, particularly models involving semiconductor capacity, wafer starts, yields, advanced packaging, substrates, PCBs, or other hardware constraints.
Experience with semiconductor, electronics, or AI infrastructure supply chains and an understanding of the relationships among manufacturing capacity, component availability, product demand, and revenue opportunity.
Familiarity with NVIDIA cuOpt, GPU-accelerated optimization, or techniques that combine deterministic optimization with AI or machine learning.

Responsibilities

Own and extend a production linear programming optimization model.
Develop constraint matrices, objective functions, dual-variable extraction logic, and diagnostics.
Maintain a rigorous grasp of the system's mathematical behavior.
Translate evolving physical supply chain realities — including new wafer nodes, packaging architectures, component categories, capacity limits, yields, and lead times — into mathematical formulations the optimization engine can solve.
Analyze shadow prices, sensitivities, and other LP diagnostics to identify the economic impact of supply constraints and turn model results into actionable insights for executive planning, procurement, and supplier discussions.
Maintain the integrity of model inputs and assumptions, understanding data lineage, schemas, dependencies, and the downstream implications of changes or inaccuracies.
Partner directly with supply chain, procurement, operations, and engineering teams to identify high-value planning decisions, quantify constraints, stress-test assumptions, and develop scenarios that improve supply and resource management.
Serve as a quantitative thought partner to senior supply chain leadership, challenging assumptions, evaluating boundary conditions, and evolving the model architecture as NVIDIA’s products and supply network become increasingly complex.
Explore opportunities to augment the deterministic optimization foundation with AI, machine learning, GPU-accelerated optimization, and NVIDIA technologies such as cuOpt.