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About the role
- HPC scheduler/resource manager engineer crafting scheduling strategies for large datacenter clusters. Driving cutting-edge innovations in AI and GPU computing with top scientific partners and technologies.
Responsibilities
- Provide engineering solutions and prototypes to enable efficient resource management and job scheduling for large scale clusters
- Drive next generation requirements and features for schedulers in at scale clusters
- Ensure technical relationships with internal and external engineering teams
- Assist system architects and machine learning/deep learning engineers in building creative solutions based on NVIDIA technology
- Be an internal reference for scheduling and resource management concepts and methodologies among the NVIDIA technical community
- Test, evaluate, and benchmark new technologies and products and work with vendors, partners and peers to improve functionality and optimize performance
Requirements
- BS, MS, or PhD in Engineering, Mathematics, Physics, Computer Science, or equivalent experience
- 12+ years of experience designing and running scheduling and resource management systems in large datacenter/AI/HPC solutions
- Knowledge and experience with resource management / scheduling code bases: SLURM preferred, other implementations (LSF, SGE, Torque...)
- Proven understanding of performance clusters, infrastructure and workload patterns
- Experience using and installing Linux-based server platforms
- C/Python/Bash/Lua programming/scripting experience
- Experience working with engineering or academic research community supporting HPC or deep learning
- Strong teamwork and both verbal and written communication skills.
Benefits
- equity
- benefits
Job title
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Experience level
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Degree requirement
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