Senior Deep Learning Software Infrastructure Engineer
$224k - $431k • Remote • US, CA, Remote
Posted 1mo ago
Job Location
US, CA, Remote
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Infrastructure Engineer
About the job
NVIDIA is seeking a Deep Learning Software Infrastructure Engineer to advance its Autonomous Vehicles project. In this role, you will be responsible for building and scaling the training libraries and infrastructure necessary for end-to-end autonomous driving models. By enabling training on thousands of GPUs and massive datasets, you will significantly accelerate iteration speed and enhance safety, working collaboratively with research and platform teams across NVIDIA.
Responsibilities
- Develop, scale, and harden deep learning infrastructure libraries and frameworks for multi-thousand GPU cluster training.
- Enhance efficiency across the training stack, including data loaders, distributed training, scheduling, and performance monitoring.
- Construct robust training pipelines and libraries to manage massive video datasets and facilitate rapid experimentation.
- Collaborate with researchers, model engineers, and internal platform teams to boost efficiency, minimize stalls, and improve training availability.
- Take ownership of core infrastructure components like orchestration libraries, distributed training frameworks, and fault-resilient training systems.
- Partner with leadership to ensure infrastructure scales with increasing GPU capacity and dataset size while maintaining developer efficiency and stability.
Requirements
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.
- 12+ years of professional experience in building and scaling high-performance distributed systems, preferably in ML, HPC, or large-scale data infrastructure.
- Extensive knowledge of deep learning frameworks (PyTorch preferred), large-scale training techniques (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.
- Strong systems background including datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, and schedulers (Slurm, Kubernetes, etc.).
- Proficiency in Python with experience developing production-grade libraries, orchestration layers, and automation tools.
- Ability to collaborate effectively with multi-functional teams (ML researchers, infra engineers, product leads) and translate requirements into robust systems.
Benefits
- Equity
- Benefits