Senior Deep Learning Engineer

Requirements:

• 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.

Nice to haves:

• 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.

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.