Senior Deep Learning Systems Architect

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $224,000–$356,500 / yr
Posted: 58 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $213k

This role $290k

$158k most similar roles pay here $378k

This role pays more than 93% of similar roles. Most pay $180,000–$246,665 — the shaded band above. At the midpoint, this role pays about $290k versus about $213k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Deep Learning Systems Architect

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As a Senior Deep Learning Systems Architect on the deep learning architecture team, you will design hardware accelerator and processor architectures for next-generation mobile, embedded, and datacenter platforms. You will contribute to features that advance the state of AI by collaborating with researchers, hardware architects, and software engineers to co-design systems from conception through prototyping. Your daily work involves analyzing deep learning techniques from first principles, identifying performance bottlenecks, and developing analytical models to improve current systems. The role requires expertise in machine learning, specifically deep neural networks, and experience with training frameworks like PyTorch, TensorFlow, or JAX. You will utilize C++, Python, and potentially CUDA, OpenCL, OpenACC, MPI, and OpenMP. This position focuses on mapping AI workloads to underlying hardware to solve complex problems in data analytics and machine learning applications.

Skills

Deep Learning Neural Networks PyTorch TensorFlow JAX C++ Python CUDA OpenCL OpenACC MPI OpenMP HPC Computer Architecture Performance Analysis Numerical Analysis GPU Computing

What you'll do

Design hardware accelerator and processor architectures for next-generation mobile, embedded, and datacenter platforms.
Co-design system architecture from initial conception through specification and prototyping phases.
Analyze deep learning workloads to determine how they map to underlying hardware and systems.
Identify performance bottlenecks and propose technical solutions to improve current systems and methods.
Perform first-principles analysis of deep learning techniques to build out analytical models.
Develop and implement prototypes to test and validate architectural ideas.
Benchmark system performance to prove the effectiveness of proposed optimizations.

What we're looking for

MS degree or equivalent experience in computer science, computer architecture, electrical engineering, or a related field.
PhD degree is an acceptable alternative to the required master's degree or equivalent experience.
10+ years of relevant work experience in the field.
Strong background in machine learning with a focus on Deep Neural Networks and solid understanding of DL fundamentals.
Experience adapting and training DNNs for various tasks.
Experience developing code for one or more major DNN training frameworks such as PyTorch, TensorFlow, or JAX.
Proficiency in numerical analysis, performance analysis, and optimization.
Programming fluency in C++ and Python.

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