Full-Time

Deep Learning Engineer

Distributed Task-Based Backends

Confirmed live in the last 24 hours

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI computing solutions

Automotive & Transportation
Enterprise Software
AI & Machine Learning
Gaming

Compensation Overview

$148k - $287.5kAnnually

+ Equity

Senior, Expert

Company Historically Provides H1B Sponsorship

Remote in USA

Category
Deep Learning
AI & Machine Learning
Required Skills
Scikit-learn
Python
Tensorflow
Pytorch
Machine Learning
C/C++

You match the following NVIDIA's candidate preferences

Employers are more likely to interview you if you match these preferences:

Degree
Experience
Requirements
  • BS, MS or PhD degree in Computer Science, Electrical Engineering or related field (or equivalent experience)
  • 5+ years of relevant industry experience or equivalent academic experience after BS
  • Proficient with Python and C++ programming
  • Strong background with parallel and distributed programming, preferably on GPUs
  • Hands-on development skills using Machine Learning frameworks (e.g. PyTorch, TensorFlow, Jax, MXNet, scikit-learn etc.)
  • Understanding of Deep Learning training in distributed contexts (multi-GPU, multi-node)
Responsibilities
  • Develop extensions to popular Deep Learning frameworks, that enable easy experimentation with various parallelization strategies!
  • Develop compiler optimizations and parallelization heuristics to improve the performance of AI models at extreme scales
  • Develop tools that enable performance debugging of AI models at large scales
  • Study and tune Deep Learning training workloads at large scale, including important enterprise and academic models
  • Support enterprise customers and partners to scale novel models using our platform
  • Collaborate with Deep Learning software and hardware teams across NVIDIA, to drive development of future Deep Learning libraries
  • Contribute to the development of runtime systems that underlay the foundation of all distributed GPU computing at NVIDIA
Desired Qualifications
  • Experience with deep-learning compiler stacks such as XLA, MLIR, Torch Dynamo
  • Background in performance analysis, profiling and tuning of HPC/AI workloads
  • Experience with CUDA programming and GPU performance optimization
  • Background with tasking or asynchronous runtimes, especially data-centric initiatives such as Legion
  • Experience building, debugging, profiling and optimizing multi-node applications, on supercomputers or the cloud

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC). These products help developers, data scientists, and IT administrators perform complex tasks efficiently. NVIDIA differentiates itself from competitors by focusing on technological advancements and a diverse range of applications, including cloud-based services like NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. The company's goal is to drive innovation in computing and provide effective solutions for a wide array of clients, from gamers to enterprises.

Company Stage

IPO

Total Funding

$19.5M

Headquarters

Santa Clara, California

Founded

1993

Growth & Insights
Headcount

6 month growth

0%

1 year growth

0%

2 year growth

-1%
Simplify Jobs

Simplify's Take

What believers are saying

  • Acquisition of VinBrain enhances NVIDIA's AI-driven healthcare solutions.
  • Investment in Nebius Group boosts NVIDIA's AI infrastructure capabilities.
  • Partnership with Serve Robotics aligns with NVIDIA's focus on robotics and AI applications.

What critics are saying

  • Increased competition from AI startups like xAI challenges NVIDIA's market position.
  • Serve Robotics' rapid expansion may lead to financial strain if market growth lags.
  • Integration challenges from VinBrain acquisition may affect NVIDIA's operational efficiency.

What makes NVIDIA unique

  • NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.
  • The Omniverse platform enhances NVIDIA's capabilities in industrial AI and digital twins.
  • NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

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Benefits

Company Equity

401(k) Company Match