Machine Learning Engineer (F/M) – Internship

Greenerwave; Paris; Presencial; EUR 11.000 - 18.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Lunch contribution

CSE benefits

Office in Paris 2nd arrondissement

Monthly team events

Mentorship from internship supervisor

Descripción de la vacante

Greenerwave seeks an ambitious final-year engineering student to join our team in Paris as an internship in modelling and training stability for digital twin antennas. You will explore physics-based models, train them, and learn to compare approaches with controlled experiments.

You’ll work with Algorithms and Modelling, RF and Software teams, document results, and grow your skills in Python, PyTorch, and numerical optimisation while contributing to robust, reproducible research in a

Formación

• Final-year engineering student in applied mathematics, simulation or modelling.
• Experience with optimisation or calibrating models using experimental data is a plus.
• Strong Python skills and familiarity with PyTorch or similar tool.

Responsabilidades

• Understand physics-based antenna models and how they are trained.
• Diagnose and improve stability by evaluating projection, reparameterisation or penalties.
• Improve stopping criteria, initialisation and reuse of trained models.
• Test code, document data and training conditions for reproducibility.

Conocimientos

Python programming

Optimization

Linear algebra

Teamwork

Educación

Final-year engineering student

Herramientas

PyTorch

Git

Linux

GPU computing

Descripción del empleo

Why this role is key

The digital twins of our antennas are used to configure our technology to steer electromagnetic waves. These models combine a physical understanding of our antennas with trained parameters. This internship aims to further improve the robustness and reliability of our model training.

Reporting to Steph, Software Engineer, you will investigate the factors that can influence training stability and evaluate different approaches to improving it. Working with our Algorithms and Modelling, Radio Frequency and Software teams, you will learn to connect physical modelling, numerical optimisation and experimental analysis through controlled, reproducible comparisons.

Your responsibilities

Understanding the model and its training process

• Understand our physics-based antenna models and how they are trained,
• Reproduce a training run and measure the variability in the resulting performance.

Diagnosing and improving stability

• Set up monitoring of relevant quantities and automatically detect diverging training runs,
• Investigate and implement projection, reparameterisation or penalty methods to enforce model convergence at each optimisation step,
• Evaluate the impact of these methods on accuracy and computation time.

Optimising and comparing training runs

• Improve stopping criteria, initialisation and the reuse of previously trained models,
• Compare several model variants under controlled conditions,
• Analyse the model’s physical and numerical mechanisms to investigate their influence on the results,

Documentation and reproducibility

• Test the code developed and maintain it under version control,
• Document the data and training conditions to ensure reproducibility of the results,
• Present the methods investigated and the results obtained in reports supported by quantitative data.

Interested in joining us ? Don’t meet all the criteria? Above all, we are looking for someone who is motivated, curious and ready to learn, within a team that will support their skills development.

You are currently in your final year at an engineering school (or an equivalent university programme), specialising in applied mathematics, simulation or modelling. You have some initial experience in optimisation or in calibrating models using experimental data, and you are interested in wave physics. Your technical skills You have a solid foundation in applied mathematics, particularly linear algebra and constrained optimisation, You can program in Python and have some initial experience with PyTorch or a similar tool, Knowledge of electromagnetism would be appreciated, Familiarity with Git or Linux, or experience with GPU computing, would be an additional asset, You have a good command of written and spoken English, enabling you to work in an international environment. Your approach to work You enjoy working in a team and communicate easily with people from a variety of backgrounds, You are thorough and approach problems methodically, You enjoy exploring different approaches and proposing creative solutions to technical problems, You are curious and take the initiative to move your work forward, You can work independently while seeking support from the team when needed, If you use AI tools for coding, you critically assess the code and calculations they produce, You can write clear, well-structured documents to share your results and document your work.

Reasons to work at Greenerwave A financial contribution towards your lunches, Benefits offered by our Social and Economic Committee (CSE), Offices located in the heart of Paris, in the 2nd arrondissement, Monthly team events and evenings out to spend time together outside of project work, Discussions with technical teams to discover the technologies you are working on and better understand their applications, A multicultural environment bringing together people with varied expertise, where you can learn from others, Close guidance and support from your internship supervisor to help you progress throughout your internship and prepare to launch your career.