Nvidia Senior Deep Learning Engineer – Autonomous Vehicles 

United States, Texas 

245250651

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.

What you’ll be doing:

• 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.

What we need to see:

• 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.

Ways to stand out from the crowd:

• 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.