Senior Deep Learning Engineer – Autonomous Vehicles

Details

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, NVIDIA is applying AI to define the next era of computing — where GPUs act as the brains of computers, robots, and self-driving cars that can understand the world. The team is seeking a Senior Deep Learning Systems Engineer to build and scale training libraries and infrastructure for end-to-end autonomous driving models, enabling training on thousands of GPUs and massive datasets.

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 technologies (DDP/FSDP, NCCL, tensor and pipeline parallelism).
• Experience with performance profiling and optimizing training stacks.
• 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.
• 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 technical leadership experience, establishing guidelines for ML systems engineering.
• Familiarity with reinforcement learning at scale, especially for simulation-heavy workloads.

Compensation & Benefits

• Base salary range: 224,000 USD - 356,500 USD (determined based on location, experience, and internal pay equity).
• Eligible for equity and additional benefits (see NVIDIA benefits).

Other details

• Location: Santa Clara, CA, United States.
• Employment type: Full time.
• Applications accepted at least until October 6, 2025.
• NVIDIA is an equal opportunity employer and fosters a diverse work environment.

#deeplearning