Senior Applied Deep Learning Research Scientist, Efficiency

Nvidia

Actively hiring
Santa Clara, CA · Seattle, WA Posted 114 days ago $192,000–$304,750 / year
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At a glance

AI generated

TL;DR

Join our Applied Deep Learning Research (ADLR) – Efficiency team as a Senior Scientist to advance the efficiency of deep learning models by reducing energy consumption and improving speed. You will research low-bit number representations and pruning techniques, co-design future neural network architectures and optimizers, and innovate with new algorithms that enhance efficiency while maintaining accuracy. Your work involves running large-scale experiments on Nvidia GPUs and collaborating across the company to optimize hardware, software, and deep learning architectures for efficiency. Key skills include a PhD in AI, computer science, or related fields, 5+ years of industrial research experience, expertise in modern DL frameworks like PyTorch or TensorFlow, proficiency in Python, and a strong background in quantization, pruning, numerics, and efficient architectures. Your contributions will significantly impact the deep learning community through open-source projects and publications.

Skills

Python CUDA TensorFlow PyTorch Kubernetes Docker CI/CD Prometheus Grafana PostgreSQL Git Jupyter Markdown GitHub Slack Zoom Nvidia GPUs OpenSource LLM_training

What you'll do

  • Research low-bit number representations and pruning effects on neural network accuracy.
  • Develop new algorithms to enhance deep learning efficiency without sacrificing accuracy.
  • Conduct large-scale experiments to validate the impact of efficiency improvements.
  • Innovate numeric formats and architecture enhancements for neural networks.
  • Analyze and optimize existing state-of-the-art neural networks for better efficiency.

What we're looking for

  • PhD in AI, CS, CE, math or related field, or 5+ years of industrial research experience.
  • Expertise in state-of-the-art neural network architectures, optimizers, and LLM training.
  • Proficiency with modern DL frameworks and inference engines, fluency in Python.
  • Track record of publications and ability to conduct large-scale experiments.
  • Strong interest and experience in neural network efficiency, quantization, pruning.
  • Background in computer architecture and GPU computing, CUDA programming.

Market check

Salary context

This $192,000–$304,750 range sits above 85% of similar postings on FindRole.

Peer median band

$152,000–$236,500

Median floor and ceiling across peers.

Typical midpoint (25–75%)

$162,000–$235,750

Middle half of comparable postings.

Based on 240 comparable postings.

* 240 is the maximum number of comparable postings sampled.

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 801 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 797 roles with salary data.

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