Play a pivotal role in defining of a modular, scalable platform to seamlessly bridge training and deployment workflows—enabling tight integration of deployment tooling with training frameworks such as Megatron and Nemo
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 support for inference optimization techniques such as speculative decoding and LoRA
Collaborate with teams across NVIDIA to use performant kernel implementations within the 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.

Requirements

Masters, PhD, or equivalent experience in Computer Science, AI, Applied Math, or related field
8+ 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
Strong algorithms and programming fundamentals
Good written and verbal communication skills and the ability to work independently and collaboratively in a fast-paced environment.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

Deep LearningPythonPyTorchTorchDynamotorch.exporttorch.compileinference optimizationspeculative decodingLoRAperformance analysis

Soft Skills

software designdebuggingtest designwritten communicationverbal communicationindependent workcollaborative workfast-paced environment

Certifications

MastersPhD