Senior Deep Learning Performance Architect - LPU

at Nvidia

📍 United States

USD 152,000-287,500 per year

SENIOR

✅ Hybrid

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 @ 4 Algorithms @ 6 CUDA @ 3 Deep Learning @ 7 GPU @ 3 HPC MPI @ 3 Machine Learning @ 7 Mathematics @ 4 Profiling @ 4 Python @ 6

Details

NVIDIA seeks a Senior Deep Learning Performance Architect to help push AI inference performance boundaries through hardware-software co-design. The role focuses on developing performance strategies, guiding future GPU architecture decisions, and advancing AI efficiency.

Responsibilities

Design novel GPU and system architectures to advance AI inference performance and efficiency.
Construct, investigate, and test popular deep learning algorithms and applications.
Analyze the relationship between hardware and software architectures and its influence on future algorithms and applications.
Build efficient power and performance models of the AI inference stack, capturing the information needed to guide next-generation hardware architecture.
Collaborate with software, research, and product teams to guide the direction of AI.

Requirements

MS or PhD in a relevant field such as computer science, electrical engineering, or mathematics, or equivalent experience.
At least 5 years of relevant experience.
Strong mathematical foundation in machine learning and deep learning.
Expert programming skills in C, C++, and/or Python.
Familiarity with GPU computing, including CUDA or similar technologies, and high-performance computing stacks such as MPI and OpenMP.
Strong knowledge of computer architecture, including relevant coursework.

Preferred Qualifications

Experience with systems-level performance modeling, profiling, and analysis.
Experience characterizing and modeling system-level performance, conducting comparison studies, and documenting and publishing results.
Experience improving AI inference workloads by developing CUDA kernels or compilers for custom ASIC hardware.

Compensation and Benefits

Base salary range: $152,000–$241,500 for Level 3 or $184,000–$287,500 for Level 4, depending on location, experience, and comparable employee compensation.
Eligible for equity and benefits.
Full-time position.
Applications accepted at least until July 26, 2026.