What you’ll be doing
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.
What we need to see
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.
Ways to stand out from the crowd
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.
- Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 168,000 USD – 264,500 USD for Level 4; and 200,000 USD – 322,000 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
