Senior Deep Learning Performance Architect

at Nvidia

USD 184,000-356,500 per year

SENIOR

✅ On-site

Tech Stack
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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 Deep Learning @ 7 GPU @ 7 HPC JAX @ 4 LLM Machine Learning @ 6 Performance Analysis @ 4 Profiling @ 4 PyTorch @ 4 Python @ 7 TensorRT @ 4

Details

NVIDIA is seeking a Senior Deep Learning Performance Architect to analyze and develop next-generation architectures that accelerate artificial intelligence, deep learning, and high-performance computing applications. The role focuses on performance analysis, performance modeling, and AI/deep learning architecture.

Responsibilities

Develop innovative architectures to advance deep learning performance and efficiency.
Analyze performance, cost, and power trade-offs by developing analytical models, simulators, and test suites.
Analyze the interplay between hardware and software architectures, future algorithms, programming models, and applications.
Evaluate performance, power, and area (PPA) for hardware features and system-level architectural trade-offs.
Develop high-level simulators in C++ and Python.
Collaborate with software, product, and research teams to guide the direction of deep learning hardware and software.

Requirements

Master's or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
6 or more years of relevant professional experience.
Strong background in GPU or deep learning ASIC architecture for distributed training and/or inference across multiple chips and nodes.
Experience with performance modeling, architecture simulation, profiling, and analysis.
Solid foundation in machine learning and deep learning.
Understanding of modern transformer-based architectures and their performance at scale.
Strong programming skills in Python, C, and C++.

Preferred Qualifications

Experience with deep neural network training, inference, and optimization using leading frameworks such as PyTorch, JAX, and TensorRT.
Familiarity with advanced optimizations and hardware/software co-design for large language model training and inference.
Exposure to using artificial intelligence to accelerate software engineering.
Self-motivation and creative and critical thinking skills.

Benefits

The role includes eligibility for equity and benefits. NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer. Applications will be accepted at least until June 7, 2026.