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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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As a Senior Deep Learning Engineer – Autonomous Vehicles, you will join the autonomous vehicles project to build and scale training libraries and infrastructure for end-to-end driving models. You will be responsible for crafting, scaling, and hardening deep learning infrastructure for multi-thousand GPU clusters while improving efficiency across data loaders, distributed training, scheduling, and performance monitoring. Your daily work involves building robust pipelines for massive video datasets and owning core components like orchestration libraries and fault-resilient systems. The role requires expertise in PyTorch, DDP/FSDP, NCCL, tensor/pipeline parallelism, Python, and C++. You will also leverage knowledge of datacenter networking, parallel filesystems, and schedulers to solve complex problems related to large-scale training infrastructure for autonomous driving models. This position focuses on enabling rapid experimentation and improving safety through high-performance distributed systems.
Build and scale deep learning infrastructure libraries for training on multi-thousand GPU clusters.
Optimize the training stack including data loaders, distributed training, scheduling, and performance monitoring.
Develop robust pipelines to process massive video datasets and enable rapid experimentation.
Own core components such as orchestration libraries, distributed training frameworks, and fault-resilient systems.
Improve training availability by minimizing stalls and enhancing efficiency across the infrastructure.
Ensure infrastructure scales with growing GPU capacity while maintaining developer stability and efficiency.
What we're looking for
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 in ML, HPC, or large-scale data infrastructure.
Extensive knowledge in deep learning frameworks (PyTorch preferred), large scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.
Strong systems background including datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, and schedulers like Slurm or Kubernetes.
Proficiency in Python and C++ for writing production-grade libraries, orchestration layers, and automation tools.
Experience scaling large GPU training clusters with over 1,000 GPUs (preferred).
Contributions to open-source ML systems libraries such as PyTorch, NCCL, FSDP, schedulers, or storage clients (preferred).
Expertise in fault resilience, high availability, and reinforcement learning at scale (preferred).
Reinforcement Learning
Python
C++
Deep Learning
GPU Clusters
Kubernetes
SLURM
High-Performance Computing
Data Pipelines
Simulation
Closed-Loop Simulation
PPO
GRPO
NuRec
Cosmos World Model
PyTorch
JAX
TensorFlow
Python
C++
CUDA
Kubernetes
SLURM
Deep Learning
Reinforcement Learning
LLMs
MLOps
HPC
Distributed Training
Sim-to-Real
Natural Language Processing
Graphics
Python
C++
SQL
Machine Learning
Computer Vision
LLMs
VLMs
Agentic Workflows
Data Science
Fault Tree Analysis
Verification and Validation
Systems Engineering
Data Analysis
Statistical Analysis
Software Engineering
Autonomous Driving
AI Foundation Models
Robotics
CUDA
System Architecture
Large-scale ML Systems
Prediction
Decision
Planning
Control
Performance Tuning
Embodied AI
High-Performance Computing