Location: Gorgonzola, Italy
In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and space, our architects design innovative solutions that make our tomorrow's possible.
Gorgonzola hosts Navigations Air Systems (NAS) domain. The activity of NAS domain: avionics, navigation and radar systems for civil air traffic and technologies supporting the activities of civilian and military pilots, ATC controllers and technicians of several airlines, air traffic control agencies and Air Force around the world. Italy is the Group’s Competence center for the design, production and delivery of Navigation Aids products and Competence Centre for the delivery of non-Radar Surveillance Products.
Job title: AI Intern
As part of our digital transformation, we are launching an innovative Proof of Concept to develop an AI-assisted tool capable of automating the initial drafting of an FMEA. The AI should analyze unstructured and structured exports (e.g. BOMs) and cross-reference them with historical reliability files to predict failure modes and their local effects.
Key Responsibilities
In the Operations team, you will:
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Analyze electronic data: understand and parse unstructured (pdf drawings) and structured outputs from standard EDA software (e.g., Mentor Graphics, Cadence).
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Develop an AI/Data pipeline: design and implement a Python-based AI strategy, combining NLP and Computer Vision techniques (LLMs and LVMs) with graph-based approaches to map circuit topologies and understand component relationships.
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Integrate historical data: connect your algorithm to historical failures files to accurately predict how specific components (e.g., resistors, capacitors, ICs) fail within their specific circuit context.
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Generate FMEA reports: structure the AI’s output into a standardized FMEA format that human engineers can review and validate.
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Test and validate: Work closely with hardware engineers to validate the AI's logic on a simple, baseline electronic board to prove the concept's viability.
Requirements:
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Currently pursuing a Master’s degree or in the final year of an engineering School. Preferred majors: Electrical Engineering or Computer Science /AI.
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Strong programming skills in Python.
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Experience with AI/Machine Learning concepts (NLP, LLM prompting/fine-tuning, or data structuring).
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Fundamental understanding of electronic circuits, components, and schematics.
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Familiarity with EDA tools and file formats (Netlists, BOMs) is a plus.
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Basic knowledge of reliability engineering concepts or FMEA is advantageous.
Soft Skills
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Hybrid Thinker: Ability to bridge the gap between hardware engineering and software/AI development.
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Problem Solver: A pragmatic approach to scoping AI projects (starting simple and scaling up).
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Autonomous & Curious: Eager to dive into historical technical data and experiment with new algorithmic approaches.
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Good communication skills to present your findings to both software and hardware teams.
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