Data Analysis Intern

1600 NIO USA, Inc.; San Jose (CA); On-site; USD 52,348 - 63,369; Full time

Job summary

1600 NIO USA, Inc. is seeking a candidate for a role focused on training data for language model projects. The ideal candidate is pursuing or has completed a master's or PhD in relevant fields, with strong Python skills and an interest in AI and generative models.

The position involves curating data, assisting in model training, and developing code to enhance workflows. Compensation ranges from $38 to $46 per hour, based on location and experience, excluding bonuses and benefits.

Qualifications

• Currently pursuing or recently completed a Master's or PhD degree.
• Experience with Python programming is essential.
• Familiarity with LLMs and AI research is advantageous.

Responsibilities

• Curate, preprocess, and analyze training data for LLM projects.
• Assist in training and evaluating language models.
• Develop and test Python code to interact with LLM APIs.

Skills

Python programming

Knowledge of libraries such as PyTorch or TensorFlow

API usage familiarity

Problem-solving mindset

Excellent communication skills

Education

Master or PhD degree in Computer Science, Data Science, Engineering, or related field

Job description

NIO is a pioneer and a leading company in the premium smart electric vehicle market. Founded in November2014, 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 (BaaS), as well as its proprietary autonomous driving technologies and Autonomous Driving as a Service (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, 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 ET7, a smart electric flagship sedan; and the ET5, a mid‑size smart electric sedan.

Responsibilities

• Curate, preprocess, and analyze training data for LLM projects
• Assist in training, tuning, and evaluating language models for specific use cases
• Develop and test Python code to interact with LLM APIs and internal tools
• Participate in improving model workflows through research and implementation of new methods
• Contribute to integration tasks, connecting AI models with external systems or applications
• Collaborate with team members for project deliverables, documentation, and code reviews
• Prepare research summaries or surveys on relevant topics in LLM and generative AI

Qualifications

• Currently pursuing or recently completed a Master or PhD degree in Computer Science, Data Science, Engineering, or a related field
• Experience with Python programming; knowledge of libraries such as PyTorch or TensorFlow is an advantage
• Familiarity with API usage and/or web frameworks is preferred
• Strong problem‑solving mindset and willingness to learn
• Excellent written and verbal communication skills
• Previous project or coursework experience in LLMs, generative models, or AI research
• Knowledge of prompt engineering, model fine‑tuning, or related evaluation techniques
• Experience working in a collaborative software engineering environment

Compensation

The US base salary range for this full‑time position is $38.00 – $46.00. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only and do not include discretionary bonus, equity, or benefits.