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, NIO's 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. NIO's 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 master's 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.