R&D Engineer Internship – Integration of Artificial Intelligence in the Design of Medical Applications
22 Minutes ago • All levels
Research Development
Job Description
This role involves integrating generative AI technologies (Copilot, ChatGPT, Windsurf, etc.) into system engineering processes to assist engineers in creating innovative medical applications, particularly for remote patient monitoring. The intern will explore and implement LLM solutions, automate technical document review, generate requirements, ensure traceability with regulatory standards, and connect engineering tools to AI solutions. The role also includes evaluating AI's impact on medical device development and proposing innovative use cases.
Good To Have:
Sensitivity to regulatory issues (medical devices, cybersecurity) is a plus.
Excellent level of spoken and written English.
Must Have:
Last year engineering school or Master's student, specializing in AI, IT, software engineering, or general engineering.
Knowledge of software development (Python, REST API, Git).
Strong interest in generative AI, LLMs, and their industrial applications.
Add these skills to join the top 1% applicants for this job
risk-management
github
game-texts
azure
azure-devops
git
python
Contribute to the digital transformation of medical devices by exploring and integrating Artificial Intelligence (LLM) solutions into system engineering processes.
Your role:
In this role, you will participate in the integration of generative AI technologies (Copilot, ChatGPT, Windsurf, etc.), already used at Philips, to assist engineers in creating innovative medical applications, particularly for remote patient monitoring.
You will be part of the Systems Engineering team, within R&D Acute Care Informatics, which is involved from the first product concepts to the market launch of software solutions for monitoring hospital and home patients.
Your main missions:
### Explore and integrate LLM solutions into system engineering workflows.
### Automate the review of technical documents (design inputs, architecture, risk analysis, etc.) via AI.