NVIDIA has been continually redefining computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s an outstanding legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPUs act as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you will be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are looking for an outstanding HPC scheduler/resource manager engineer for architecture, deployment and performance optimization of large datacenter cluster systems and applications. Be a key player to the most exciting computing hardware and software to driving the latest breakthroughs in artificial intelligence and GPU computing. Provide insights on at-scale system design for collecting, visualizing, and acting on a wide variety of data. You will work with the latest Accelerated computing and Deep Learning software and hardware platforms, and with many scientific researchers, developers, and customers to craft state of the art scheduling strategies which enable new systems and interconnects. You will interact with OS, GPU compute, and networking specialists to envision, develop and bring up large scale systems.
What you’ll be doing:
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.
What we need to see:
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.
Ways to stand out from the crowd:
Experience with HPC cluster administration for AI.
Experience deploying containerized services.
Experience with orchestrators (e.g. Kubernetes).
Demonstrated work with Open-Source software: building, debugging, patching and contributing code.
Experience tuning memory, storage, and networking settings for performance on Linux systems.
Exposure to monitoring and telemetry systems.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearningTop Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
