NVIDIA
Senior Deep Learning Software Infrastructure Engineer
$224K–$431K
US, CA, Remote · Senior · Full-time · Posted Sep 7, 2026
- Experience
- 12+ years of professional experience.
- Education
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.
About the job
Build and scale deep-learning training infrastructure for NVIDIA’s autonomous driving models, enabling large-scale GPU training and faster experimentation.
What you'll do
- Develop and harden libraries, frameworks, pipelines, and fault-resilient systems for training across large GPU clusters.
- Improve training-stack efficiency, including data loading, distributed training, scheduling, and performance monitoring.
- Collaborate with research, engineering, and platform teams to scale infrastructure while maintaining stability and developer efficiency.
What they ask 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, ideally in ML, HPC, or large-scale data infrastructure.
- Extensive knowledge of deep learning frameworks, large-scale training methods, and performance profiling.
- Strong systems background in datacenter networking, parallel filesystems, storage systems, and schedulers; proficiency in Python.
Nice to have
- Experience scaling GPU training clusters with >1,000 GPUs.
- Expertise in fault resilience, high availability, elastic training, or large-scale observability.
Pay
224,000 USD - 356,500 USD (base salary, Level 5); 272,000 USD - 431,250 USD (base salary, Level 6)
Tools
PyTorch, DDP/FSDP, NCCL, RoCE, IB, Lustre, Slurm, Kubernetes
AI focus
Build training infrastructure for end-to-end autonomous driving models using large datasets and multi-thousand-GPU clusters.
Model-written summary · the original posting is authoritative