Senior Deep Learning Engineer

NVIDIA; United States; Remote; USD 60,000 - 80,000; Full time

Job summary

A leading technology company in the United States is seeking an experienced professional to enhance inference speed for their platforms. The ideal candidate will have substantial experience in Deep Learning, strong programming skills in Python and PyTorch, and be adept in inference optimization techniques. This role involves analyzing and deploying models in production settings, making it essential to have a strong technical background and the ability to optimize workflows for efficiency.

Qualifications

• 5+ years of experience in relevant fields.
• Strong background in Deep Learning and programming skills.

Responsibilities

• Improve inference speed for Cosmos WFMs on GPU platforms.
• Carry out production deployment of Cosmos WFMs.
• Profile and analyze deep learning workloads.

Skills

Deep Learning

Python

PyTorch

Inference Optimization

TensorRT

Education

MSc or PhD in CS, EE, or CSEE

Tools

Docker

Triton Inference Server

Job description

What you'll be doing:

• Improve inference speed for Cosmos WFMs on GPU platforms.
• Effectively carry out the production deployment of Cosmos WFMs.
• Profile and analyze deep learning workloads to identify and remove bottlenecks.

What we need to see:

• 5+ years of experience.
• MSc or PhD in CS, EE, or CSEE or equivalent experience.
• Strong background in Deep Learning.
• Strong programming skills in Python and PyTorch.
• Experience with inference optimization techniques (such as quantization) and inference optimization frameworks, one of: TensorRT, TensorRT-LLM, vLLM, SGLang.

Ways to stand out from the crowd:

• Familiarity with deploying Deep Learning models in production settings (e.g., Docker, Triton Inference Server).
• CUDA programming experience.
• Familiarity with diffusion models.
• Proven experience in analyzing, modeling, and tuning the performance of GPU workloads, both inference and training.