Reinforcement Learning Engineer

Since 2014, Wiremind has positioned itself as a technical 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.

At Wiremind, the Data Science team is responsible for the development, monitoring and evolution of all ML-powered forecasting and optimization algorithms in use in our Revenue Management systems. Our algorithms are divided in 2 parts:

• A modelling of the demand using ML models (e.g. deep learning, boosted trees) trained on historical data in the form of time-series

• Constrained optimizations problems solved using linear programming techniques

The team is shaped to have all profiles necessary to constitute an autonomous department (DevOps, software and data engineering, data science, AIML, operational research) and works on a modern technical stack composed of argo-workflow (pipelines orchestrator), MLFlow (models & experiments tracking) and in-house python packages.

Recently, we have begun exploring new ways of solving our revenue optimization problems using Reinforcement Learning techniques instead of linear programming.

As an Optimization Intern, you will take a part in this research effort by:

• Exchanging on a daily basis with the data, ML and product teams to perfect your business comprehension

• Proposing new ideas to solve revenue optimization problems

• Implementing, testing and evaluating these ideas in a controlled environment

• Backend: Python 3.11+

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

• Datastores: Druid, ClickHouse, postgresql

• Common ML libraries/tools: pytorch, scikit-learn, pandas, numpy

• Reinforcement Learning: Gymnasium, stable-baselines3

• Model versioning and registry tool: MLFlow

• Gitlab / Kubernetes for CI/CD

• Prometheus/Grafana and Kibana for operations & monitoring

• You are pursuing a Master's Degree in Engineering, Data Science, Applied Mathematics or a similar field

• You have prior knowledge of usual Machine Learning techniques and good practices

• You are looking for an end-of-study internship

• You are passionate about addressing business challenges through innovative technological solutions

• You are committed to maintaining high-quality standards in all aspects of your work

What would be a plus 

• A first experience, internship or school project in Reinforcement Learning

• Knowledge of Reinforcement Learning theory

By joining us, you will integrate:

• A self-financed startup with a strong technical identity

• 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 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…)

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

2. A Screening Manager with Soukaina, the OR Engineer

3. A Technical Test/Business case to prepare for

4. A presentation at our offices in the presence of 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.