Internship / Thesis Student for Edge AI Optimization Research & Engineering (AI)

TL;DR

Internship / Thesis Student for Edge AI Optimization Research & Engineering (AI): Researching and developing state-of-the-art model compression and inference-time optimizations for Small Language Models and Vision Language Models on NXP’s embedded systems with an accent on efficient generative architecture design and performance evaluation. Focus on exploring novel compression techniques, optimizing LLM inference, and integrating developed methods into embedded systems.

Location: Onsite in Eindhoven, Hamburg, or Munich

Company

hirify.global is an Artificial Intelligence Competence Center focusing on research, innovation, and engineering in Edge AI.

What you will do

• Explore, design, and implement model compression and inference optimization techniques for LLMs and VLMs.
• Evaluate performance of optimized models and systems on NXP’s embedded systems.
• Contribute to the integration of developed methods into NXP’s embedded systems.
• Document research findings and contribute to scientific publications or invention disclosures.

Requirements

• Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• Excellent English communication skills.
• Very good understanding of AI/ML concepts (LLMs, VLMs, Agentic AI) and experience with PyTorch/TensorFlow.
• Familiarity with model compression techniques (quantization, pruning, knowledge distillation) and LLM inference optimization.
• Experience with Python and modern software development practices.
• Must be registered as a student for a full-time internship (36/40 hours/week) for a minimum of six months.

Nice to have

• Experience with edge AI deployment (e.g., TFLite, ONNX, ExecuTorch).

Culture & Benefits

• Opportunity to contribute to cutting-edge research and development in Edge AI.
• Gain hands-on experience in a supportive, high-tech environment.
• Work within a collaborative, diverse, and multinational team.
• Explore new technologies and contribute to impactful AI research.