Internship: AI Agents for Systems Quality Engineering

This internship focuses on understanding and improving the full lifecycle of AI agents in Systems Quality Engineering — from idea and use-case definition to prototype, evaluation, documentation, and potential reuse.

The Systems Quality Engineering (SysQE) team within Philips Innovation Engineering supports engineering teams in applying quality engineering methods in a regulated medical technology environment. The team works on topics such as requirements quality, design quality, risk management, design reviews, DFMEA/PFMEA support, design verification, process validation, and quality decision-making across the product development lifecycle.

Within this engineering quality environment, AI agents can increasingly support productivity, consistency, and technical decision-making. Many opportunities exist to help engineers find, interpret, and apply quality knowledge more effectively. However, turning these ideas into robust, maintainable, scalable, and trusted AI-agent solutions remains a challenge.

To enable fast experimentation and learning, the internship can initially focus on agent concepts that do not require deep domain expertise, before extending to more SysQE-specific use cases.

Your role:

• Understand SysQE workflows and identify where AI agents can support or assist engineers and quality specialists.
• Analyze existing or conceptual AI-agent ideas and their underlying assumptions.
• Select one or more practical use cases with clear value for Systems Quality Engineering.
• Design and implement simple agent prototypes or agent-supported workflows to test different approaches.
• Evaluate agent behavior, usability, maintainability, reliability, source traceability, and integration aspects.
• Experiment with interaction, reuse, or orchestration between multiple agents, prompts, knowledge sources, or workflows.
• Involve SysQE engineers and relevant stakeholders to check practical usefulness and learn from real engineering needs.
• Document findings, design choices, patterns, limitations, and lessons learned.

Based on the insights gained, the student will propose and prototype new or improved agents, demonstrating how Systems Quality Engineering knowledge can be transformed into practical, usable, and sustainable digital support.

You're the right fit if:

• You are currently pursuing a Master degree in Computer Science, Software Engineering, AI, Data Science, Systems Engineering, Industrial Engineering, Biomedical Engineering, or a related field.
• You have interest in AI agents, automation, applied software engineering, knowledge management, and engineering quality.
• You have knowledge of statistics.
• You are comfortable with experimenting, learning by doing, and working in an exploratory setting.
• You have the ability to translate complex information into practical solutions.
• You have good communication skills and willingness to interact with engineers, quality specialists, and stakeholders.
• You are a self-starter, clearly state your opinion, act sharply, and like redesigning processes.
• Experience with Python, Microsoft 365 Copilot, Copilot Studio, Power Platform, prompt engineering, or AI-agent frameworks is a plus.
• Experience or interest in medical technology, quality engineering, systems engineering, risk management, or regulated product development is a plus.

How we work together

We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week. Onsite roles require full-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.

About Philips

Philips is a health technology company focused on expanding access to quality healthcare.

If you’re interested in this role and have many, but not all, of the experiences needed, you may still be the right candidate for this opportunity at Philips.