Deep Learning Software Infrastructure Engineer

NVIDIA is seeking a Deep Learning Software Infrastructure Engineer to propel its Autonomous Vehicles project forward.

In this role, you will build and scale training libraries and infrastructure that make end-to-end autonomous driving models possible.

Responsibilities

• Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
• Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
• Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
• Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
• Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
• Partnering with leadership to ensure infrastructure scales with growing 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 building and scaling high-performance distributed systems, ideally in ML, HPC, or large-scale data infrastructure.
• Extensive knowledge in deep learning frameworks (PyTorch is preferred), large scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.
• Strong systems background: datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, schedulers (Slurm, Kubernetes, etc.).
• Proficiency in Python with experience writing production-grade libraries, orchestration layers, and automation tools.
• Ability to work closely with multi-functional teams (ML researchers, infra engineers, product leads) and translate requirements into robust systems.

Nice to Have

• Shown experience scaling large GPU training clusters with >1,000 GPUs.
• Expertise in fault resilience and high availability, including elastic training and large-scale observability.
• Tried leadership skills as a hands-on technical authority, encouraging others and establishing guidelines for ML systems engineering.

You will also be eligible for equity and benefits.