Description
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 extensive, diverse, high-quality data. We're 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're looking for hands-on experience training and deploying Large Language Models (LLMs) and Vision-Language Models (VLMs) in production environments. You'll collaborate with other researchers, software engineers to bring pioneering AI models from prototype to production.
Responsibilities:
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
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 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