Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
CI/CD @ 4
Algorithms @ 4
Mathematics @ 4
API @ 4
CUDA @ 4
GPU @ 4
AI @ 4
HPC @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. NVIDIA BioNeMo is building the computational foundation for the next generation of biological discovery. The MD Simulation Engineering team builds GPU-native simulation software that powers molecular dynamics at scale, working at the intersection of GPU computing and computational biology to deliver high-performance math primitives and kernel engineering across NVIDIA GPU generations.
Responsibilities
- Build, implement, and optimize CUDA kernels for core molecular dynamics (MD) simulation primitives.
- Deliver end-to-end GPU-accelerated simulation math to external partners and the broader MD ecosystem.
- Integrate simulation primitives into major MD engines.
- Drive CI/CD infrastructure for multi-SKU kernel builds, automated performance regression testing, and cross-simulator validation across NVIDIA GPU generations.
- Collaborate with Applied Science teams to evaluate new algorithms and translate research prototypes into production-quality, shipped software.
Requirements
- 8+ years of software engineering experience with a strong background in CUDA and GPU programming.
- Deep proficiency in C and C++; comfortable navigating and contributing to large, sophisticated codebases.
- Strong foundation in high-performance computing (HPC).
- Familiarity with molecular dynamics simulation concepts.
- Experience building or supplying to scientific software libraries, simulation engines, or developer-facing GPU APIs.
- BS/MS in Computer Science, Computational Science, Physics, Applied Mathematics, or a related field, or equivalent experience.
Ways to Stand Out
- Supplied to or deeply used a major MD simulation engine.
- Experience with GPU compiler toolchains and kernel delivery mechanisms.
- Hands-on knowledge of SPME, Ewald summation, or other long-range electrostatics methods at the implementation level.
- PhD or equivalent experience in computational chemistry, biophysics, mathematical modeling, or computer science with a focus on HPC or scientific computing.
- Experience with mixed-precision or tensor-core-aware algorithm builds for scientific workloads and contributions to open-source MD simulation or GPU computing projects.
Compensation and Benefits
- Base salary ranges by level and location:
- Level 4: 184,000 USD - 287,500 USD
- Level 5: 224,000 USD - 356,500 USD
- Eligible for equity and benefits (see www.nvidiabenefits.com and https://www.nvidia.com/en-us/benefits/).
Additional Information
- Applications for this job will be accepted at least until May 28, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer committed to fostering a diverse work environment.