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The AI Robotics Research Intern will focus on dexterous manipulation, utilizing large-scale data to improve robotic behaviors through human interaction insights. Responsibilities include developing scalable frameworks, processing data, and optimizing robotic policies for physical deployment.

Design and implement scalable inference systems for AI models, optimizing performance across various platforms while ensuring software quality and integration with hardware teams.

The Data Analysis Intern will preprocess and analyze training data for LLM projects, assist in training language models, and develop Python code to interact with APIs.

The role involves automating testing for automotive software, developing scalable test frameworks, managing CI/CD pipelines, and collaborating with cross-functional teams to ensure quality software delivery.

The Senior OS/Kernel Engineer will design and develop NIO's vehicle operating systems, optimize system performance, and collaborate with engineering teams on production quality software.

The Staff System Engineer will conduct research on systems software, design components, and optimize performance for NIO's vehicle operating systems.

Lead the architectural design for AI hybrid inference systems, mentor team, and guide development of high-availability orchestration engines while driving innovation.

Research and implement methods to transfer human-object interaction data into robotic dexterous manipulation policies. Build large-scale data pipelines, generative embodied-AI models, unified cross-embodiment training, and sim-to-real deployment; validate on real multi-fingered or humanoid hardware and produce a technical manuscript or demo.

Design and implement high-performance, scalable inference systems for LLMs and VLMs across cloud, edge, and hybrid platforms. Develop and optimize custom kernels for hardware accelerators (GPU/NPU/DSP), integrate tensor/model parallelism, caching, and quantization, and collaborate with system and hardware teams to deploy production-grade AI inference.

Optimize Large Language Models for vehicle applications, focusing on algorithmic efficiency and deployment on distributed systems.