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Senior Deep Learning Software Engineer, Inference and Model Optimization
NVIDIA
full-time
Posted on:
Location Type: Remote
Location: Remote • California • 🇺🇸 United States
Visit company websiteSalary
💰 $148,000 - $287,500 per year
Job Level
Senior
Tech Stack
PythonPyTorch
About the role
- Train, develop, and deploy state-of-the generative AI models like LLMs and diffusion models using NVIDIA's AI software stack
- Leverage and build upon the torch 2.0 ecosystem (TorchDynamo, torch.export, torch.compile, etc...) to analyze and extract standardized model graph representation from arbitrary torch models for our automated deployment solution
- Develop high-performance optimization techniques for inference, such as automated model sharding techniques (e.g. tensor parallelism, sequence parallelism), efficient attention kernels with kv-caching, and more
- Collaborate with teams across NVIDIA to use performant kernel implementations within our automated deployment solution
- Analyze and profile GPU kernel-level performance to identify hardware and software optimization opportunities
- Continuously innovate on the inference performance to ensure NVIDIA's inference software solutions (TRT, TRT-LLM, TRT Model Optimizer) can maintain and increase its leadership in the market
- Play a pivotal role in architecting and designing a modular and scalable software platform to provide an excellent user experience with broad model support and optimization techniques to increase adoption
Requirements
- Masters, PhD, or equivalent experience in Computer Science, AI, Applied Math, or related field
- 3+ years of relevant work or research experience in Deep Learning
- Excellent software design skills, including debugging, performance analysis, and test design
- Strong proficiency in Python, PyTorch, and related ML tools (e.g. HuggingFace)
- Strong algorithms and programming fundamentals
- Good written and verbal communication skills and the ability to work independently and collaboratively in a fast-paced environment
- Contributions to PyTorch, JAX, or other Machine Learning Frameworks is a plus
- Knowledge of GPU architecture and compilation stack, and capability of understanding and debugging end-to-end performance is a plus
- Familiarity with NVIDIA's deep learning SDKs such as TensorRT is a plus
- Prior experience in writing high-performance GPU kernels for machine learning workloads in frameworks such as CUDA, CUTLASS, or Triton is a plus
Benefits
- competitive salaries
- comprehensive benefits package
- equity
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
generative AILLMsdiffusion modelstorch 2.0automated model shardingtensor parallelismsequence parallelismGPU kernel optimizationPythonPyTorch
Soft skills
software designdebuggingperformance analysistest designwritten communicationverbal communicationindependent workcollaborative workfast-paced environment
Certifications
Masters in Computer SciencePhD in AIPhD in Applied Math
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