Senior Deep Learning Software Infrastructure Engineer

at Nvidia

USD 224,000-431,200 per year

SENIOR

✅ On-site

Tech Stack
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1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Deep Learning @ 4 Distributed Systems @ 4 Experimentation GPU @ 4 HPC InfiniBand @ 7 Kubernetes @ 7 Leadership @ 6 Machine Learning @ 4 Mentoring @ 6 NCCL @ 4 Networking @ 7 Observability @ 6 Performance Monitoring Profiling @ 4 PyTorch @ 4 Python @ 6 Slurm @ 7

Details

NVIDIA is seeking a Deep Learning Software Infrastructure Engineer to support its Autonomous Vehicles project. The role focuses on building and scaling training libraries and infrastructure for end-to-end autonomous driving models, enabling training across thousands of GPUs and massive datasets while accelerating iteration speed and improving safety. The position involves close collaboration with research and platform teams across NVIDIA.

Responsibilities

Craft, scale, and harden deep learning infrastructure libraries and frameworks for training on multi-thousand-GPU clusters.
Improve efficiency throughout the training stack, including data loaders, distributed training, scheduling, and performance monitoring.
Build robust training pipelines and libraries for massive video datasets and rapid experimentation.
Collaborate with researchers, model engineers, and internal platform teams to improve efficiency, minimize stalls, and increase 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 sizes while maintaining developer efficiency and stability.

Requirements

Bachelor’s, master’s, or doctoral degree in Computer Science, Electrical or Computer Engineering, or a related field, or equivalent experience.
12 or more years of professional experience building and scaling high-performance distributed systems, ideally in machine learning, high-performance computing, or large-scale data infrastructure.
Extensive knowledge of deep learning frameworks, preferably PyTorch; large-scale training technologies including DDP, FSDP, NCCL, tensor parallelism, and pipeline parallelism; and performance profiling.
Strong systems background, including datacenter networking such as RoCE and InfiniBand, parallel filesystems such as Lustre, storage systems, and schedulers such as Slurm and Kubernetes.
Proficiency in Python, including experience developing production-grade libraries, orchestration layers, and automation tools.
Ability to work with multifunctional teams, including machine learning researchers, infrastructure engineers, and product leads, and translate requirements into robust systems.

Preferred Qualifications

Experience scaling large GPU training clusters with more than 1,000 GPUs.
Expertise in fault resilience and high availability, including elastic training and large-scale observability.
Demonstrated leadership as a hands-on technical authority, including mentoring others and establishing guidelines for machine learning systems engineering.

Compensation and Benefits

The expected base salary ranges are USD 224,000–356,500 for Level 5 and USD 272,000–431,250 for Level 6. Base salary is determined by location, experience, and other objective job-related factors. The position is also eligible for equity and benefits.