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NVIDIA

Deep Learning Performance Software Engineer

NVIDIA is hiring a Deep Learning Performance Software Engineer in Beijing, China. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
  • The posting mentions C, Llvm and Mlir among its tools and skills.
  • Level classes it as a senior role.
  • NVIDIA has 2,700+ other open roles on Level.

AI in this role

Develops and optimizes deep learning compilers, DSLs, and GPU-accelerated software kernels for AI workloads.

cllvmmlirtvmxla
deep-learningcompiler-designgpu-accelerationperformance-optimizationalgorithms

We are now looking for a Deep Learning Performance Software Engineer!

We are expanding our research and development for deep learning. We seek excellent Software Engineers and Senior Software Engineers to join our team. We specialize in developing GPU-accelerated Deep learning software. Researchers around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in numerous areas. Join the team that builds software to enable new solutions. Your ability to work in a fast-paced customer-oriented team is required and excellent communication skills are necessary.

What you’ll be doing:

  • Develop compilers and DSLs for deep learning workloads

  • Design and implement highly optimized deep learning kernels

  • Continuously improve the compiler architecture for current and next generation chips

  • Perform performance analysis on emerging AI workloads and integrate with AI frameworks

What we need to see:

  • Master's or Ph.D degree (or equivalent experience) in relevant discipline (CE, CS&E, CS, AI)

  • Excellent C/C++ programming and software design skills

  • Experience with XLA, TVM, MLIR, LLVM, deep learning models and algorithms

  • 3+ years of relevant work experience

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most brilliant and talented people on the planet working for us. If you're creative and autonomous, we want to hear from you!

How we rate this

Deep Learning Performance Software Engineer at NVIDIA rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Deep learningCompiler DesignGPU AccelerationPerformance OptimizationAlgorithmsCLlvmMlir

Also common in software engineer jobs at this level

Pay

Typical software engineer job
$168k

This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs

Questions you could be asked

  1. Tell me about a project where deep learning was part of your work. What did you do?
  2. Tell me about a project where compiler design was part of your work. What did you do?
  3. Tell me about a project where gpu acceleration was part of your work. What did you do?
  4. Tell me about a project where performance optimization was part of your work. What did you do?
  5. Tell me about a project where algorithms was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Deep learning, Compiler Design, GPU Acceleration, Performance Optimization, and Algorithms. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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