Senior Deep Learning Software Infrastructure Engineer
About The Position
NVIDIA is seeking a Deep Learning Software Infrastructure Engineer to advance its Autonomous Vehicles project. The role involves building and scaling training libraries and infrastructure that enable end-to-end autonomous driving models, including training across thousands of GPUs and massive datasets. The work is intended to accelerate iteration and improve safety, in collaboration with research and platform teams across NVIDIA. The engineer will own and improve core infrastructure for distributed deep learning training, including orchestration libraries, training frameworks, and fault-resilient systems. The position also partners with leadership to ensure infrastructure scales with GPU capacity and dataset growth while preserving developer efficiency and system stability.
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, ideally in ML, HPC, or large-scale data infrastructure.
- Extensive knowledge of deep learning frameworks (PyTorch preferred), large-scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.
- Strong systems background, including datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, and schedulers such as Slurm or Kubernetes.
- Proficiency in Python and experience writing production-grade libraries, orchestration layers, and automation tools.
- Ability to work closely with multifunctional teams and translate requirements into robust systems.
Nice To Haves
- Experience scaling large GPU training clusters with more than 1,000 GPUs.
- Expertise in fault resilience and high availability, including elastic training and large-scale observability.
- Leadership skills as a hands-on technical authority, encouraging others and establishing guidelines for ML systems engineering.
Responsibilities
- Craft, scale, and harden deep learning infrastructure libraries and frameworks for training on multi-thousand-GPU clusters.
- Improve efficiency throughout the training stack, including data loaders, distributed training, scheduling, and performance monitoring.
- Build robust training pipelines and libraries for massive video datasets and rapid experimentation.
- Collaborate with researchers, model engineers, and internal platform teams to improve efficiency, minimize stalls, and increase training availability.
- Own core infrastructure components, including orchestration libraries, distributed training frameworks, and fault-resilient training systems.
- Partner with leadership to scale infrastructure with growing GPU capacity and dataset size while maintaining developer efficiency and stability.
Benefits
- Eligible for equity and benefits.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
