Senior Deep Learning Systems Architect

Nvidia

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Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $224,000–$356,500 / yr
Employment: Full-time
Posted: 104 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $206k

This role $290k

$152k $378k

below market most similar roles pay here above market

This role pays more than 95% of similar roles. Most pay $177,250–$235,750 — the blue band above. At the midpoint, this role pays about $290k versus about $206k 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 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Deep Learning Systems Architect

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JOB TITLE: Senior Deep Learning Systems Architect The Senior Deep Learning Systems Architect joins the deep learning architecture team to design hardware accelerator and processor architectures for next-generation mobile, embedded, and datacenter platforms. This role involves co-designing systems from conception through prototyping, identifying performance bottlenecks, and proposing solutions to accelerate current methods. You will perform comprehensive analyses from first principles to build analytical models and implement prototypes for various deep learning techniques. Key responsibilities include collaborating with researchers and engineers to map AI workloads to underlying hardware. Required skills include deep neural network fundamentals, experience with training frameworks like PyTorch, TensorFlow, or JAX, and proficiency in C++ and Python. Preferred expertise includes GPU computing via CUDA, OpenCL, or OpenACC, as well as HPC standards like MPI and OpenMP.

Skills

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

What you'll do

Design hardware accelerator and processor architectures for next-generation mobile, embedded, and datacenter platforms.
Co-design system architectures from initial conception through specification and prototyping.
Map various AI and deep learning workloads to underlying hardware and systems.
Identify performance bottlenecks and propose solutions to accelerate current systems and methods.
Perform first-principles analyses of deep learning techniques and system optimizations to build analytical models.
Implement and benchmark prototypes to test and prove architectural ideas.
Develop code for deep learning training frameworks such as PyTorch, TensorFlow, or JAX.

What we're looking for

MS or PhD degree in computer science, computer architecture, electrical engineering, or a related field.
10+ years of relevant work experience.
Strong background in machine learning with a focus on Deep Neural Networks and DL fundamentals.
Experience adapting and training DNNs for various tasks.
Experience developing code for one or more DNN training frameworks such as PyTorch, TensorFlow, or JAX.
Proficiency in numerical analysis, performance analysis, and optimization.
Proficiency in computer architecture.
Programming fluency with C++ and Python (preferred).
Work experience with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP) (preferred).

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