Research Engineer Generative-AI, open source LLM models Data science and fine-tuning models

Found in: Appcast Linkedin GBL C2 - 2 weeks ago

San Jose, United States NIO Full time

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:


• Using one of the open source LLM models, you will research and apply the latest and state-ofthe-

art techniques to create and POC an AI-based application using proprietary data by

training, fine-tuning, and augmenting the base model.


Qualifications:


• PhD or master’s degree with publication and research projects in Computer Science,

Computer Engineering, Applied Mathematics, Data Science.

• Experience with open source LLM models such as lama 2, Mistral, Claude 2, Grok-1

• Experience fine-tuning and merging LLM models with LoRA, PEFT, Quantization, Tokenization

• Proficient in Python, AI-related training and inferencing tools such as PyTorch, vLLM, Ray

• Knowledge on prompting techniques such as Iterative refinement, Feedback loops, Zeroshot,

Few-shot, CoT

• Knowledge on different model evaluation tasks such as HellaSwag, TruthfulQA, MMLU

• Good understanding of natural language processing, machine learning, AI generated content

development

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