Intern – AI (R&D Trainee)
Valeo
Key Responsibilities
- As an AI R&D Intern at Valeo, you will contribute directly to innovation teams on the following tracks:
- Assisting in exploring, designing, and optimizing breakthrough technical solutions targeted at enabling intuitive driving and lowering vehicle CO2 emissions.
- Collaborating with mechanical and core R&D engineering teams to bridge hardware specifications with smart software capabilities.
- Participating in data aggregation, preprocessing, and exploratory evaluation workflows to train underlying computational models.
- Supporting senior solutions architects and research engineers in testing and debugging algorithmic frameworks within structured test scenarios.
- Drafting technical reports, documentation sheets, and baseline performance comparisons for emerging prototype initiatives.
- Aligning with green sustainability practices and corporate engineering methodologies to maintain top-tier structural standards.
Skills & Eligibility
- Degree Alignments: Pursuing or recently graduated with a full-time Engineering degree (B.E. / B.Tech / M.E. / M.Tech) from a recognized university.
- Preferred Fields: Computer Science, Artificial Intelligence, Data Science, Mechanical Engineering, Robotics, or related technical disciplines showcasing an elective interest in AI integration.
- AI & ML Foundations: Elementary academic or project exposure to foundational AI concepts, machine learning algorithms, or data analytics models.
- Programming Knowledge: Baseline scripting literacy in languages standard to data processing or engineering tasks (such as Python, C++, or MATLAB).
- Analytical Thinking: Strong structural troubleshooting habits with an ability to parse physical and software-driven parameters.
- Collaboration Mindset: Eagerness to operate within a multicultural, multi-disciplinary R&D workspace that values active group documentation and mutual learning.
- Degree Alignments: Pursuing or recently graduated with a full-time Engineering degree (B.E. / B.Tech / M.E. / M.Tech) from a recognized university.
- Preferred Fields: Computer Science, Artificial Intelligence, Data Science, Mechanical Engineering, Robotics, or related technical disciplines showcasing an elective interest in AI integration.
- AI & ML Foundations: Elementary academic or project exposure to foundational AI concepts, machine learning algorithms, or data analytics models.
- Programming Knowledge: Baseline scripting literacy in languages standard to data processing or engineering tasks (such as Python, C++, or MATLAB).
- Analytical Thinking: Strong structural troubleshooting habits with an ability to parse physical and software-driven parameters.
- Collaboration Mindset: Eagerness to operate within a multicultural, multi-disciplinary R&D workspace that values active group documentation and mutual learning.
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