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
Algorithms @ 7
CUDA @ 4
Communication @ 6
Data Structures @ 4
GPU @ 4
Mathematics @ 7
Parallel Programming @ 4
Performance Optimization @ 4
Prioritization @ 6
- 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's Developer Technology Engineering team is a global network of experts working on accelerated computing. The team develops solutions that optimize large application workloads, eliminate system bottlenecks, and advance computing technologies.
Responsibilities
- Research and develop techniques to accelerate leading cloud service provider workloads on NVIDIA's computing platform, including advanced CPUs, GPUs, and interconnects.
- Work directly with key customers to analyze and optimize complex workloads for performance on current and next-generation hardware.
- Collaborate with libraries, tools, system software architecture, hardware, and research teams to influence next-generation programming models, software, and architectures.
- Investigate application performance, design parallel algorithms, and implement optimizations in GPU-accelerated computing environments.
- Publish findings in developer blogs, conferences, and workshops.
- Contribute application expertise that influences future hardware and software products.
Requirements
- Master's degree in Computer Science, Computer Engineering, or a related computationally focused science discipline, or equivalent experience.
- 8 or more years of relevant work experience or research.
- Proficiency in C/C++ with a deep understanding of software design, programming techniques, and algorithms.
- Experience with parallel programming, ideally CUDA C/C++.
- Hands-on experience with low-level performance optimization.
- In-depth knowledge of CPU and GPU architecture fundamentals.
- Strong mathematics skills, including linear algebra, for problem-solving and performance modeling.
- Good communication, organization, and prioritization skills.
Preferred Qualifications
- Experience designing highly optimized parallel algorithms and data structures for applications with a high bytes-to-compute ratio, including processing compressed data directly and kernel fusion.
- Experience optimizing end-to-end application performance across multiple software layers, from the operating system to high-level frameworks.
- Experience influencing hardware feature design using application and domain knowledge.
Benefits
- Equity.
- Employee benefits.
- NVIDIA is an equal opportunity employer committed to an inclusive work environment.
Applications will be accepted at least until August 9, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
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