Senior Deep Learning Engineer – Autonomous Vehicles

Details

NVIDIA is seeking a Senior Deep Learning Systems Engineer to advance the Autonomous Vehicles project by building and scaling training libraries and infrastructure for end-to-end autonomous driving models. The role focuses on enabling training on multi-thousand GPU clusters and improving iteration speed, safety, and developer productivity through robust, high-performance infrastructure.

Responsibilities

• Craft, scale, and harden deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
• Improve efficiency across the training stack: data loaders, distributed training, scheduling, and performance monitoring.
• Build robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
• Collaborate with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
• Own core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
• Partner 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 of deep learning frameworks (PyTorch preferred) and large-scale training (DDP/FSDP, NCCL, tensor and pipeline parallelism).
• Strong systems background including datacenter networking (RoCE, InfiniBand), parallel filesystems (Lustre), storage systems, and schedulers (Slurm, Kubernetes).
• Proficiency in Python and C++, with experience writing production-grade libraries, orchestration layers, and automation tools.
• Experience with performance profiling and optimizing large-scale training workflows.
• Ability to work closely with cross-functional teams (ML researchers, infra engineers, product leads) and translate requirements into robust systems.

Ways to stand out

• Experience scaling large GPU training clusters with >1,000 GPUs.
• Contributions to open-source ML systems libraries (e.g., PyTorch, NCCL, FSDP, schedulers, storage clients).
• Expertise in fault resilience and high availability, including elastic training and large-scale observability.
• Hands-on leadership experience as a technical authority for ML systems engineering.
• Familiarity with reinforcement learning at scale, particularly for simulation-heavy workloads.

Compensation & Benefits

• Base salary range: 224,000 USD - 356,500 USD (base determined by location, experience, and pay of employees in similar positions).
• Eligible for equity and benefits (see NVIDIA benefits page).

Other details

• Applications accepted at least until July 3, 2026. This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is an equal opportunity employer and committed to fostering an inclusive work environment.