Automotive AI Intern: LLMs, RAG & Model Checking

Aumovio SE; Singapore; On-site; SGD 20,000 - 38,000; Part time

Job summary

AUMOVIO SE is seeking a student from a technical field to contribute to building LLM-based solutions for automotive requirements understanding and verification. You will work on structuring OEM requirements, developing RAG pipelines, and exploring agentic AI workflows under expert guidance.

The role focuses on model checking, code generation, and collaborating with AI researchers and engineers to validate complex system designs, with practical exposure to tools and frameworks in a hands-on

Qualifications

• Currently pursuing a degree in Computer Science, AI, Software Engineering, or mathematics/related field.
• Basic knowledge of machine learning, NLP, or LLMs.
• Proficient in Python programming.

Responsibilities

• Analyze and structure technical requirements from automotive OEMs.
• Develop LLM-based solutions for requirement understanding, classification, and validation.
• Build and evaluate RAG pipelines for engineering documents and domain knowledge.
• Explore agentic AI workflows for complex engineering tasks.
• Apply model checking and formal verification to validate system requirements and designs.
• Investigate using LLMs to generate or synthesize formal models and code for model-checking tools.
• Design experiments, evaluate results, and document findings.
• Collaborate with AI researchers, software engineers, and domain experts.

Skills

Python programming

NLP basics

Team collaboration

Education

Bachelor student in Computer Science / AI / related field

Tools

Python

LangChain

LlamaIndex

Git

Docker

NuSMV / SPIN knowledge

Job description

AUMOVIO SE is seeking a student from a technical field to contribute to building LLM-based solutions for automotive requirements understanding and verification. You will work on structuring OEM requirements, developing RAG pipelines, and exploring agentic AI workflows under expert guidance.

The role focuses on model checking, code generation, and collaborating with AI researchers and engineers to validate complex system designs, with practical exposure to tools and frameworks in a hands-on