Since 2014, Wiremind has positioned itself as a tech company transforming the world of transport and events with a 360° approach combining UX, software, and AI.

Our expertise lies primarily in optimizing and marketing our clients' capacity. We work on various projects such as ticket forecasting and pricing, 3D optimization of air freight or scraping competitor prices. Our applications are the preferred tool of companies such as SNCF, United Airlines, Qatar Airways or even PSG to visualize, analyze and optimize their capacity.

Dynamic and ambitious, we strive to maintain our technical DNA which is the engine of our success. The company, profitable and self-financed since its creation 10 years ago, is mainly composed of engineers and experts and currently supports the growth of our business model based on "software-as-a-service" solutions.

Your missions 🚀

At Wiremind, the Data team develops, monitors, and evolves ML-powered forecasting and optimization capabilities used in our Revenue Management systems. The team consists of Machine Learning Engineers, who build and monitor ML models, and Data Engineers, who ensure robust data pipelines and smooth integration of these models into our software.

As a Machine Learning Intern, with support from more senior ML Engineers, your responsibilities will include :

• Designing/training new models (decision trees or neural networks) to improve our current models in production

• Tackling specific challenges: imbalanced data, skewed data distributions, feature engineering

• Performing in-depth model evaluation to properly assess live model performance, identify data drift, and propose improvements

• Taking part in improving our training and deployment pipelines (tests, automation, reliability) — we value quality, rigor, and reproducibility

• Surveying state-of-the-art methods and practices, and assessing what’s applicable to our domain-specific use cases

Technical stack:

• Common ML libraries/tools: LightGBM, Pytorch, Pandas, Jupyter notebooks, SHAP

• Backend: Python 3.13+ with SQLAlchemy

• Orchestration: Argo workflows over an auto-scaled Kubernetes cluster

• Datastores: PostgreSQL

• Model versioning and registry tool: MLflow

• Gitlab / Kubernetes for CI/CD

• Prometheus/Grafana and Kibana for operations

Your profile 🔍

• You are currently pursuing a Master’s Degree in data science, computer science, or applied mathematics

• You have a good understanding of fundamental machine learning concepts (e.g., classification/regression) and the underlying mathematical principles

• You have a good understanding of evaluation metrics (accuracy, recall, precision, RMSE, etc.) and statistics

• Hands-on experience with Python and standard data tools (Pandas, NumPy)

• You care about engineering quality: readable code, tests, and reproducible experiments

• You are curious, eager to learn, and comfortable exploring new topics (including prospective R&D)

• You have some experience building models (Kaggle, class, or personal projects)

What we offer for 6 months 🤌

By joining us, you will integrate:

• A self-financed startup with a strong technical DNA! 🧬

• Beautiful 800 m² offices in the heart of Paris (Bd Poissonnière) ✨

• Attractive remuneration 💪

• A caring and stimulating team that encourages skills development through initiative and autonomy

• A learning environment with concrete opportunities for evolution 🧑‍💻

You will also benefit from:

• 1 day of remote work per week 💻

• Access to WellPass at a preferential rate to maintain your well-being 🏀

• A great company culture (monthly afterworks, regular meetings on technology and products, annual off-site seminars, team-building…)

Our recruitment process 🤞

1. A Screening HR with Nicolas, the Talent Acquisition Specialist

2. A Screening Manager with Mathilde or Constant, both Head of Data

3. A Technical Test to prepare at home

4. A Technical Interview at our offices with the manager

Wiremind is committed to equality of opportunity, diversity, and fairness. We encourage all candidates with the necessary experience to apply for our job offers.