Used Tools & Technologies
GPURequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 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.
Python @ 4
Algorithms @ 7
Machine Learning @ 8
LLM @ 4
PyTorch @ 6
Deep Learning @ 8
AI @ 4
vLLM @ 4
SGLang @ 4
- 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.
Details
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing, where GPUs act as the brains of computers, robots, and self-driving cars. This role is part of the autonomous driving effort, focused on building the data engine that powers large-scale AI systems for autonomous vehicles. The team works with petascale fleets and advanced AI models.
Responsibilities
- Explore state-of-the-art LLM and VLM models for search and classification of autonomous vehicle (AV) scenarios.
- Hands-on model development such as fine-tuning large LLMs and VLMs for internal use cases.
- Collaborate with software engineers and researchers to ensure seamless integration of models from training to 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 large language models (LLMs) and vision-language models (VLMs).
- Deep understanding of transformer architectures, inference bottlenecks, and popular model architectures such as the Qwen family.
- Proficient in building and deploying models using PyTorch in production-grade environments.
- Solid programming skills in Python.
Ways to stand out
- Proven experience deploying LLMs or VLMs at scale in real-world applications using tools such as vLLM and SGLang.
- Hands-on experience with fine-tuning techniques including SFT, DPO, and GRPO.
- Proven experience in developing image and video search solutions at scale.
Compensation & Benefits
- Base salary range: 224,000 USD - 356,500 USD (determined based on location, experience, and comparable roles).
- Eligible for equity and benefits.
Additional information
- Applications for this job will be accepted at least until May 18, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment.