2 days ago
Responsibilities
- Build and scale deep learning infrastructure libraries and frameworks for multi-thousand-GPU training clusters.
- Improve data loaders, distributed training, scheduling, performance monitoring, and overall training-stack efficiency.
- Develop robust training pipelines and libraries for massive video datasets and rapid experimentation.
- Own orchestration libraries, distributed training frameworks, and fault-resilient training systems.
- Collaborate with ML researchers, model engineers, platform teams, and leadership to scale infrastructure while maintaining developer efficiency and stability.
- Provide hands-on technical leadership, establish guidelines, and improve fault resilience, high availability, elastic training, and observability.
Requirements
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.
- 12+ years of professional experience building and scaling high-performance distributed systems, preferably in ML, HPC, or large-scale data infrastructure.
- Extensive knowledge of deep learning frameworks, large-scale training methods including DDP and FSDP, NCCL, tensor and pipeline parallelism, and performance profiling.
- Strong systems background in datacenter networking, parallel filesystems, storage systems, and schedulers.
- Proficiency in Python and experience building production-grade libraries, orchestration layers, and automation tools.
- Ability to collaborate with ML researchers, infrastructure engineers, and product leads and translate requirements into robust systems.
- Experience scaling GPU training clusters with more than 1,000 GPUs, fault resilience and high availability, elastic training, and large-scale observability are preferred.
Benefits
- Eligible for equity and benefits.
- Applications accepted at least until November 10, 2026.
- This posting is for an existing vacancy.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.