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Senior Deep Learning Software Engineer, Inference

€78k - €135k • Remote • Netherlands, Remote • Full-time

Posted 12h ago

Job Location

Netherlands, Remote

Also open in US, CA, Santa Clara

Tech Stack

Remote Work Policy

Fully remote

Employment Type

Full-time

Categories

LLM Engineer

About the job

NVIDIA is seeking a Senior Software Engineer to focus on Deep Learning Inference. You will be instrumental in designing, building, and optimizing GPU-accelerated software for advanced AI applications. This role involves developing and enhancing high-performance deep learning frameworks like SGLang and vLLM, crucial for efficient large-scale model serving and inference. You will contribute to the smooth deployment and serving of cutting-edge language models and work with the deep learning community to implement and release the latest algorithms.

Responsibilities

  • Optimize, analyze, and tune deep learning models in domains such as LLM, Multimodal, and Generative AI.
  • Scale deep learning model performance across various NVIDIA accelerator architectures.
  • Contribute features and code to NVIDIA's inference libraries, including vLLM, SGLang, FlashInfer, and LLM software solutions.
  • Collaborate with cross-functional teams on frameworks, NVIDIA libraries, and innovative inference optimization solutions.

Requirements

  • Master's or PhD degree or equivalent experience in a relevant field (Computer Engineering, Computer Science, EECS, AI).
  • 5+ years of relevant software development experience.
  • Excellent C/C++ programming and software design skills.
  • Experience with training, deploying, or optimizing deep learning model inference in production is a plus.
  • Background in performance modeling, profiling, debugging, code optimization, or CPU/GPU architectural knowledge is a plus.
  • Agile software development skills are helpful.
  • Python experience is a plus.

Benefits

  • Highly competitive salaries
  • Extensive benefits package
  • Work environment that promotes diversity, inclusion, and flexibility