Senior Deep Learning Engineering - Autonomous Vehicles

at Nvidia

📍 Santa Clara, United States

USD 224,000-356,500 per year

SENIOR

✅ On-site

Tech Stack
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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.

AI Algorithms @ 7 Deep Learning @ 8 LLM Machine Learning @ 8 PyTorch @ 6 Python @ 6 SGLang @ 4 vLLM @ 4

Details

NVIDIA is building the future of autonomous driving, from silicon to full-stack AI systems powering next-generation autonomous vehicles. The team is developing the data engine behind an advanced AI platform using extensive, diverse, and high-quality data from petascale fleets.

The role focuses on developing systems and algorithms that extract intelligence from autonomous vehicle data. You will train and deploy Large Language Models (LLMs) and Vision-Language Models (VLMs) in production environments and collaborate with researchers and software engineers to bring AI models from prototype to production.

Responsibilities

Explore state-of-the-art LLM and VLM models for searching and classifying autonomous vehicle scenarios.
Develop models, including fine-tuning large LLMs and VLMs for internal use cases.
Collaborate with software engineers and researchers to integrate models from training through deployment.

Requirements

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 transformer architectures, inference bottlenecks, and popular model architectures such as the Qwen family.
Proficiency building and deploying models with PyTorch in production-grade environments.
Solid programming skills in Python.

Preferred Qualifications

Experience deploying LLMs or VLMs at scale in real-world applications using vLLM or SGLang.
Hands-on experience with supervised fine-tuning (SFT), direct preference optimization (DPO), and group relative policy optimization (GRPO) techniques.
Experience developing image and video search solutions at scale.

Compensation and Benefits

Base salary range: USD 224,000–356,500 per year.
Eligible for equity and benefits.
Salary is determined based on location, experience, and the pay of employees in similar positions.