Research Engineer LLM and Vision-AI models paralle computing - San Jose, United States - NIO

Description

Overview:

NIO Inc. is a pioneer and a leading company in the premium smart electric vehicle market. Founded in November 2014, NIOs mission is to shape a joyful lifestyle. NIO aims to build a community starting with smart electric vehicles to share joy and grow together with users.

NIO designs, develops, jointly manufactures and sells premium smart electric vehicles, driving innovations in next-generation technologies in autonomous driving, digital technologies, electric powertrains and batteries. NIO differentiates itself through its continuous technological breakthroughs and innovations, such as its industry-leading battery swapping technologies, Battery as a Service, or BaaS, as well as its proprietary autonomous driving technologies and Autonomous Driving as a Service, or ADaaS. NIOs product portfolio consists of the ES8, a six-seater smart electric flagship SUV, the ES7 (or the EL7), a mid-large five-seater smart electric SUV, the ES6 (or the EL6), a five-seater all-round smart electric SUV, the EC7, a five-seater smart electric flagship coupe SUV, the EC6, a five-seater smart electric coupe SUV, the ET9, a smart electric executive flagship, the ET7, a smart electric flagship sedan, the ET5, a mid-size smart electric sedan, and the ET5T, a smart electric tourer.

Responsabilities:

You will research and you will apply state-of-the-art technologies to optimize models and

deploy them on distributed and heterogenous hardware.

The models will be Open Source Large Language Models (LLM) and images diffusion models.

Qualifications:

PhD or masters degree with publication and research projects, in Computer Science,

Computer Engineering, Applied Mathematics, Communication, Electronics.

Knowledge of GPU architecture and bottlenecks.

Good understanding of LLM models, diffusion models and video summarization models

(LSTM/ViT/Diffusion).

Proficient in Python, AI-related training and inferencing tools such as PyTorch.

Experience with model serving technologies such as Open Neural Network Exchange (ONNX).

Experience with debugging code in distributed environments.

Knowledge of different vision and video model evaluation tasks such as Kinects400,

Moments in Time, etc.