Business Context
Test Program (TP) development is a key activity within NXP Test Engineering, requiring high quality, consistency, and strict integration into established development workflows (code reviews, CI/CD, quality checks).
Recent advances in Artificial Intelligence and Generative AI offer potential to automate and accelerate structured and repetitive parts of TP development. However, applying these technologies in an industrial context requires a clear strategy, well‑defined architecture, feasibility assessment, and compliance with NXP processes and AI governance.
Apprentice Objective
The objective of this internship is to define an AI strategy and target architecture for automatic generation of Test Programs (TP), with a focus on engineering feasibility rather than full implementation.
The apprentice will:
Propose a modular architecture for AI‑assisted TP generation
Assess feasibility and risks for each architectural block
Identify a realistic and compliant minimum viable scope
Optionally develop a proof of concept for one selected block to validate the approach
A key design constraint is usability:
The proposed solution should aim for “no setup dependencies” for end users (easy to deploy, reproducible, minimal environment configuration).
Scope of Work
The internship activities will include:
State‑of‑the‑art review of AI and Generative AI approaches applicable to code and test generation
Definition of a target architecture, including:
functional blocks and responsibilities
data flow and interfaces
integration into existing TP development workflows
Feasibility assessment per block, covering:
required inputs and dependencies
technical risks and limitations
validation and quality considerations
integration and governance constraints
Definition of recommendations and next steps
Optional implementation of one architectural block or a limited end‑to‑end slice as a proof of concept
Expected Deliverables
State‑of‑the‑art summary (focused on applicability to TP generation)
Architecture proposal (block diagram and description)
Feasibility matrix per block (Go / Conditional / No‑Go assessment)
Optional proof of concept for one selected block
Final recommendations and roadmap proposal
Candidate Profile
Master’s or Engineering student in Computer Science, Software Engineering, AI, or related field
Strong software engineering fundamentals (Python and/or Java)
Interest in AI, automation, and software architecture
Structured mindset and ability to document and communicate technical findings
Nice to have:
Knowledge of CI/CD, code quality, or test automation concepts
Familiarity with Generative AI / LLM principles