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Profile, debug, and optimize data-center and edge computer vision workloads for efficiency, latency, and throughput.
Implement and improve computer vision and image processing algorithms using CUDA.
Establish and drive product-critical performance metrics.
Influence software architecture and technical roadmaps to ensure outstanding performance.
Contribute to large codebases combining custom C++ and Python with distributed architectures (microservices, Kubernetes, Triton) to deliver computer vision at scale.
Provide technical leadership in high-performance computing to computer vision teams across NVIDIA.
Master's of Science in Computer Science or Electrical engineering (or equivalent experience)
10+ years practical experience.
Excellent software engineering fundamentals (source control, CI/CD, testing/validation, packaging, containerization, release). Proven track record developing, testing and releasing production-grade, complex software.
Proficiency with C++, CUDA, and Python.
Strong fundamentals with multi-threaded and distributed software development.
Experience with performance-critical data center applications.
Proven track record defining and driving performance metrics to ensure product success and differentiation.
Excellent written, visual, and verbal communication to present performance challenges, tradeoffs, and architectural alternatives.
Strong collaboration skills to partner with algorithm designers, application developers, and infrastructure and MLOps teams.
Ability and desire to learn new technologies.
Experience with classical and machine-learning based computer vision including ML-Ops.
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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