Senior Deep Learning Engineering - Autonomous Vehicles

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.

At NVIDIA, we’re building the future of autonomous driving — from the silicon to the full-stack AI systems that power next-generation robots on wheels. Our ability to deliver safe, scalable autonomy depends on one thing above all: Data. Extensive, diverse, high-quality data. We are seeking a  highly skilled Deep Learning Engineer to develop systems and algorithms extracting intelligence from petascale fleets. This role offers an opportunity to build the data engine powering one of the world’s most advanced AI platforms. We are looking for hands-on experience training and deploying Large Language Models (LLMs) and Vision-Language Models (VLMs) in production environments. You will collaborate with other researchers, software engineers to bring pioneering AI models from prototype to production.

What you will be doing:

• Explore SOTA LLM/VLM models for search and classification of AV scenarios

• Hands on model developments such as fine-tuning large LLM/VLMs for internal use cases

• Collaborate with software engineers and researchers to ensure seamless integration of models from training to deployment.

What we want to see:

• Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience)

• 10+ years of professional experience in deep learning or applied machine learning.

• Strong foundation in deep learning algorithms, including hands-on experience with LLMs and VLMs

• Deep understanding of general transformer architectures, inference bottlenecks, and popular model architectures such Qwen family.

• Proficient in building and deploying models using PyTorch in production-grade environments.

• Solid programming skills in Python

Ways to stand out from the crowd:

• Proven experience deploying LLMs or VLMs at scale in real-world applications using vLLM, SGLang.

• Hands-on experience with SFT, DPO, GRPO techniques for fine-tuning

• Proven experience in developing image and video search solutions at scale.

This posting is for an existing vacancy. 

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