AI Systems Programmer
2 months ago
Are you passionate about developing high-performance software?
We are seeking dedicated software developers to contribute to the design, implementation, and deployment of cuDNN: our GPU-accelerated library tailored for deep neural networks. The rise of intelligent machines powered by AI is transforming industries, and we are at the forefront of this evolution. If you believe your skills align with our mission, we would be eager to discuss how you can be a part of our innovative team.
Your Responsibilities:
- Create production-ready software that integrates into NVIDIA's AI software ecosystem, including support for optimized large language models (LLMs).
- Evaluate the performance of critical workloads, refine existing software, and suggest enhancements for future iterations.
- Collaborate with interdisciplinary teams of deep learning engineers and GPU architects to drive innovation across various applications, including generative AI, autonomous systems, computer vision, and recommendation engines.
- Stay adaptable in the fast-paced AI landscape by contributing across the codebase, focusing on API design, software architecture, performance modeling, testing, and GPU kernel development.
- Provide mentorship to junior engineers within the team.
Qualifications We Are Looking For:
- A Master's degree in computer science or a related field, or equivalent professional experience.
- Proficient programming skills in C/C++, experience with CUDA development, and familiarity with Python.
- Solid understanding of linear algebra principles.
- Awareness of current trends in machine learning.
- Experience in designing high-level software architectures.
- Background in performance analysis, profiling, and code optimization.
- Ability to work autonomously, establish project objectives, and lead development initiatives.
Preferred Qualifications:
- Expertise in GPU programming and optimization techniques (e.g., CUDA or OpenCL).
- Hands-on experience with machine learning, particularly deep learning.
- Knowledge of computer architecture and the ability to build performance models for CPUs, GPUs, or other accelerators.
- Strong problem-solving capabilities, including proficiency in algorithms and data structures.
- Extensive experience in data science, statistical analysis, and data visualization.
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