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Associate Nvidia Autonomous Driving Jobs in Missouri

NVIDIA, Stripe, DoorDash, Open AI, TMobile, Moderna, Workday, Ulta, Target, and more. Locations ... Experience driving projects autonomously through ambiguity (scoping to delivery) * Experience ...

AI & HPC Infrastructure Engineer

Saint Louis, MO · On-site

$97K - $127K/yr

... driving modernization across the entire infrastructure stack. We collaborate across the ecosystem ... Architect and deploy with NVIDIA platform tools including Base Command Manager (BCM), NGC, NCCL ...

Product Designer

California, MO · On-site

$145 - $185/hr

... for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active ... Data-driven decision making - you are comfortable driving user research, both with internal ...

You will operate with autonomy while being backed by the scale, technology, and credibility of a ... In this role you will engage with peers in-office four days a week, igniting creativity and driving ...

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Associate Nvidia Autonomous Driving information

What is the difference between Associate Nvidia Autonomous Driving vs Associate Autonomous Vehicle Software Engineer?

AspectAssociate Nvidia Autonomous DrivingAssociate Autonomous Vehicle Software Engineer
Required CredentialsBachelor's in Computer Science, Electrical Engineering, or related field; experience with AI, deep learning, and automotive systemsBachelor's or Master's in Software Engineering, Computer Science; knowledge of autonomous systems and programming
Work EnvironmentResearch labs, automotive industry, tech companies focusing on autonomous drivingAutomotive companies, tech firms, R&D centers working on autonomous vehicle software
Employer & Industry UsageUsed by Nvidia, automotive OEMs, Tier 1 suppliersUsed by automakers, autonomous vehicle startups, tech companies

The Associate Nvidia Autonomous Driving role primarily focuses on developing and testing autonomous driving systems using Nvidia's platforms, while the Associate Autonomous Vehicle Software Engineer role emphasizes software development for autonomous vehicle functionalities. Both roles require strong technical skills and industry knowledge but differ slightly in their focus areas within autonomous vehicle technology.

What are the most commonly searched types of Nvidia Autonomous Driving jobs in Missouri?

The most popular types of Nvidia Autonomous Driving jobs in Missouri are:

What cities in Missouri are hiring for Associate Nvidia Autonomous Driving jobs?

Cities in Missouri with the most Associate Nvidia Autonomous Driving job openings:

Senior Deep Learning Sofware Infrastructure Engineer

NVIDIA Corporation

California, MO • On-site

$224 - $431/hr

Other

Posted 18 days ago


Key responsibilities

  • Build and scale deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.

  • Improve efficiency of the training stack, including data loaders, distributed training, scheduling, and performance monitoring.

  • Build robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies


Job description

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.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

  • You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 10, 2026.

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.

NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry.

Learn more about NVIDIA.

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About Nvidia

Sourced by ZipRecruiter

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.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US