Senior Deep Learning Engineer – Autonomous Vehicles

NVIDIA is seeking a Senior Deep Learning Systems Engineer to propel its Autonomous Vehicles project forward. You will build and scale training libraries and infrastructure for end-to-end autonomous driving models.

Job Overview As a Senior Deep Learning Systems Engineer, you will craft, scale, and harden deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters. You will improve efficiency throughout the training stack, build robust training pipelines and libraries, collaborate with researchers and internal platform teams, and own core infrastructure components.

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 and C++, 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.
• 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.
• Tried leadership skills as a hands-on technical authority, encouraging others and establishing guidelines for ML systems engineering.
• Familiarity with reinforcement learning (RL) at scale, particularly in the context of simulation-heavy workloads.

Benefits You will also be eligible for equity and benefits.