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.
Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are in search of a Deep Learning Software Infrastructure Engineer to propel NVIDIA’s Autonomous Vehicles project forward. In this role, you will build and scale training libraries and infrastructure that make end-to-end autonomous driving models possible. By enabling training on thousands of GPUs and massive datasets, you will accelerate iteration speed and improve safety, working closely with research and platform teams across NVIDIA.
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 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.
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.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience
- 12+ years professional experience building and scaling high-performance distributed systems (ML, HPC, or large-scale data infrastructure preferred)
- Extensive knowledge in deep learning frameworks and large-scale training (PyTorch preferred; DDP, FSDP, NCCL, tensor/pipeline parallelism) and performance profiling
- Strong systems background: datacenter networking (RoCE, InfiniBand), parallel filesystems (Lustre), storage systems, and schedulers (Slurm, Kubernetes)
- Proficiency in Python and 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
- Experience scaling large GPU training clusters with >1,000 GPUs
- Expertise in fault resilience, high availability, elastic training, and large-scale observability
- Hands-on technical leadership and ability to establish ML systems engineering guidelines
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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