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What you'll be doing:
Develop, profile and optimize inference pipelines for VLMs and other AI CV models: improve throughput and latency, data loading, pre- and post-processing.
Improve the efficiency of VLM models themselves: kernel optimization in CUDA
Upstream improvements to SDKs and libraries across NVIDIA and beyond to deliver accelerated computer vision at scale.
Promote high-performance AI computer vision across NVIDIA teams and functions (Engineering, Product Management, Marketing, and more).
What we need to see:
Master's of Science in Computer Science or Electrical engineering or equivalent experience.
8 years practical experience or equivalent
Expertise in AI computer vision (VLMs, Vision Transformers, Diffusion models). Proven track record using its software ecosystem (PyTorch, HuggingFace, vLLM) to develop and release production-grade software.
Excellent software engineering fundamentals (source control, CI/CD, testing/validation, packaging, containerization, release).
Proficiency with Python, C++ and CUDA (kernel optimization)
Experience developing cloud applications (REST APIs, gRPC).
Excellent written, visual, and verbal communication to present performance challenges, tradeoffs, and architectural alternatives.
Curiosity and drive to learn new technologies and partner across teams and functions.
Ways to Stand Out from the Crowd:
Expertise in classical, non-ML computer vision
Strong fundamentals with system-level performance: multi-threaded, multi-process and distributed software development.
Grounding in mathematical fundamentals such as linear algebra, numerical methods, statistics, and exploratory data analysis.
History of creativity and innovation around performance in multiple problem domains.
You will also be eligible for equity and .
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