Math Libraries Intern, cuEST - Summer 2027
NVIDIA is hiring a Math Libraries Intern, cuEST - Summer 2027 in Santa Clara, United States. It pays $20-$94/hr and Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include CUDA, C and Python.
- Level classifies it as an internship.
- Level lists 2,700+ other open roles at NVIDIA.
- The role was posted in the last few days.
AI in this role
Develop GPU-accelerated algorithms and numerical methods for quantum chemistry math libraries in CUDA.
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world. NVIDIA is looking for software engineering interns to join the math libraries team building cuEST, a CUDA-X math library that accelerates quantum chemistry on NVIDIA GPUs. Launched in March 2026, cuEST provides accelerated APIs for essential functions in electronic structure computations, starting with Gaussian-basis quantum chemistry. These capabilities power applications ranging from drug discovery to semiconductor process development. We are constantly working to improve the feature set, performance, and technology stack of cuEST. If the idea of helping expand the frontiers of electronic structure theory excites you, come join our team and help us build GPU-accelerated software used by quantum chemists around the world.
During your internship, you will work with senior engineers on the cuEST team who will mentor you and guide you in developing highly optimized algorithms and numerical methods for quantum chemistry. Projects may involve implementing new quantum chemistry primitives, defining APIs, analyzing and improving GPU performance, finding solutions for difficult numerical corner cases, and other software engineering work that goes into shipping a production CUDA-X library.
What you’ll be doing:
- Prototype and develop GPU-accelerated algorithms for Gaussian-basis quantum chemistry primitives in cuEST.
- Analyze the performance of CUDA implementations, identify opportunities for improvement, and implement them.
- Contribute across the engineering lifecycle, from design and testing to code that ships in the library.
- Collaborate with team members and other stakeholders to understand how quantum chemistry applications use cuEST and what they need from it.
What we need to see:
- Pursuing a BS, MS, or PhD degree in Computational Chemistry, Computational Physics, Computer Science, Applied Math, or an equivalent program area.
- Excellent C/C++ or Python programming, debugging, performance analysis, and test design skills.
- Strong algorithms and numerical methods fundamentals.
- Ability to work independently and lead your own development effort.
- Good teamwork, communication, and documentation habits.
Ways to stand out from the crowd:
- GPU programming experience (CUDA or OpenCL) is desired but not required.
- Coursework or research experience in quantum chemistry or electronic structure theory is desired but not required.
- Knowledge of floating-point arithmetic and numerical error analysis.
- Knowledge of CPU or GPU hardware architecture.
- Experience using agentic AI tools to improve the speed and quality of software development.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative and autonomous, we want to hear from you!
Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rate for this position is 20 USD – 94 USD.You will also be eligible for Intern benefits.
Applications for this job will be accepted at least until November 6, 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.How we rate this
Math Libraries Intern, cuEST - Summer 2027 at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where algorithms was part of your work. What did you do?
- Tell me about a project where numerical methods was part of your work. What did you do?
- Tell me about a project where performance analysis was part of your work. What did you do?
- Tell me about a project where quantum chemistry was part of your work. What did you do?
- Tell me about a project where software engineering was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Algorithms, Numerical Methods, Performance Analysis, Quantum Chemistry, and Software Engineering. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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