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Software Engineer, Deep Learning Cudnn - New College Grad 2024
Company | NVIDIA |
Address | Santa Clara, CA, United States |
Employment type | FULL_TIME |
Salary | |
Category | Computer Hardware Manufacturing,Software Development,Computers and Electronics Manufacturing |
Expires | 2023-09-25 |
Posted at | 8 months ago |
Do you love writing fast code and crafting software systems to solve complex problems? We are looking for hardworking software engineers to help design, build, and ship cuDNN: our GPU-accelerated library of primitives for deep neural networks. Intelligent machines powered by AI computers that can learn, reason, and interact with people are no longer science fiction. This is truly an extraordinary time. The era of AI has begun, and we are powering it. If this role seems like a good match for your skills and interests, tell us why you think you might be a great fit for our team, and we'd love to tell you more about what we do!
- Develop production-quality software that ships as part of NVIDIA's AI software stack, including optimized large language model (LLM) support.
- Adapt to the constantly evolving AI industry by being agile and excited to contribute across the codebase, including API design, software architecture, performance modeling, testing, and GPU kernel development.
- Analyze the performance of important workloads, tuning our current software, and proposing improvements for future software.
- Work with cross-collaborative teams of deep learning software engineers and GPU architects to innovate across applications like generative AI, autonomous driving, computer vision, and recommender systems.
- B.S. degree or higher in computer science (or similar) or equivalent experience
- Experience with performance analysis, profiling, and code optimization.
- Strong programming skills in C/C++ development and familiarity with Python.
- Good understanding of linear algebra.
- Excellent problem solving skills, including applications of algorithms and data structures.
- Familiarity with the latest trends in machine learning.
- Strong experience with data science, statistical analysis, and visualization.
- Experience with computer architecture and building performance models for CPUs, GPUs, or other accelerators.
- Practical experience with machine learning, especially deep learning.
- Previous work on large complex codebases with many other developers, especially libraries, compilers, or system software.
- GPU programming and optimization expertise (e.g. CUDA or OpenCL).
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