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 @ 6
Algorithms
CUDA @ 4
Deep Learning @ 7
GPU @ 4
HPC
LLM @ 7
OpenCL @ 4
Performance Analysis @ 4
Performance Optimization @ 4
Profiling @ 4
PyTorch @ 6
SGLang
vLLM
- 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
We are looking for a Senior Deep Learning Algorithms Engineer to optimize LLM and omni models. This role focuses on performance analysis and optimization across the hardware and software stack, from GPU architecture to deep learning frameworks, to maximize deep learning workload performance. The role offers the opportunity to influence hardware and software roadmaps at NVIDIA.
Responsibilities
- Enable and optimize state-of-the-art open models, including Nemotron and Cosmos, on NVIDIA's accelerated inference software stack.
- Contribute new features, fix bugs, and deliver production code to open-source frameworks such as TRT-LLM, vLLM, SGLang, and FlashInfer.
- Profile and analyze bottlenecks across the full inference stack to improve inference performance.
- Benchmark state-of-the-art offerings and perform competitive analysis for NVIDIA's software and hardware stack.
- Co-design with partner teams to develop the next generation of AI models and services.
Requirements
- PhD in Computer Science, Electrical Engineering, Computer Science and Electrical Engineering, or equivalent experience.
- 3+ years of experience.
- Strong background in deep learning and neural networks, particularly inference.
- Experience with performance profiling, analysis, and optimization, especially for GPU-based applications.
- Proficiency in PyTorch or equivalent AI frameworks, or in developing high-performance computing applications.
- Deep understanding of computer architecture and familiarity with GPU architecture fundamentals.
Preferred Qualifications
- Proven experience with processor- and system-level performance optimization.
- Deep understanding of modern LLM and diffusion architectures.
- Strong algorithm fundamentals.
- GPU programming experience with CUDA or OpenCL.
Compensation and Benefits
- Level 3 base salary: USD 152,000–241,500 per year.
- Level 4 base salary: USD 184,000–287,500 per year.
- Eligibility for equity and benefits.
- Applications will be accepted at least until May 9, 2026.
NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. NVIDIA uses AI tools in its recruiting processes.
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