Tech Stack
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.
AI
API @ 4
Algorithms
CI/CD
CUDA @ 7
GPU @ 4
HPC @ 4
Mathematics @ 4
- 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. Its a unique legacy of innovation thats fueled by great technologyand amazing people. Today, were 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 whats never been done before takes vision, innovation, and the worlds best talent. As an NVIDIAN, youll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
NVIDIA BioNeMo is building the computational foundation for the next generation of biological discovery. We are looking for a Senior Software Engineer to join our MD Simulation Engineering team, whose mission is to enable biological simulation engines at scale.
This team builds the GPU-native simulation software that powers molecular dynamics at scale. We work at the intersection of GPU computing and computational biology delivering high-performance math primitives that simulation software depends on to fully exploit NVIDIA hardware across GPU generations. The work spans kernel engineering, software architecture, and direct collaboration with the MD simulation ecosystem. If you want to define how scientific simulation is accelerated on modern hardware and see your work in the hands of researchers worldwide, this is the role.
Responsibilities
- Build, implement, and optimize CUDA kernels for core MD simulation primitives
- Be responsible for the end-to-end delivery of 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++; comfort navigating and chipping in to large, sophisticated codebases
- Strong foundation in high-performance computing
- 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 from the Crowd
- You have supplied to or deeply used a major MD simulation engine
- Experience with GPU compiler toolchains, 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 build for scientific workloads as well as contributions to open-source MD simulation or GPU computing projects
Benefits
With competitive salaries and a generous benefits package (www.nvidiabenefits.com), we are widely considered to be one of the technology worlds most desirable employers. You will also be eligible for equity and benefits (https://www.nvidia.com/en-us/benefits/).