ML Engineer

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 distributing 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 unconstrained 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

You will be joining a team shaped to have all profiles necessary to constitute an autonomous department (devops, software and data engineering, data science, AIML, operational research). There, you will leverage state-of-the-art AI/ML methods and ironclad validation processes to deliver robust, interpretable prediction systems.

As a ML Engineer, with support from a Lead Data Scientist, you will take part in the development and improvement of new features and algorithms for our SaaS applications, using a mixture of proven traditional model-based methods as well as recent breakthroughs in Deep Learning for regression problems.

In practice – even though there is no typical day – you can expect to:

• Develop, maintain, and propose improvements for our training framework via Argo + MLFlow

• Deploy and monitor of models in production

• Oversee implementations of new clients from the data analysis phase, modeling, deployment, and hyper-supervision of the first optimization runs in production

• Develop analytics and AB testing tools to help us continuously improving our models

• Backend: Python 3.11+ with SQLAlchemy

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

• Datastores: Druid and postgresql

• Common ML libraries/tools: TensorFlow/Keras, LightGBM, XGBooost, Pandas, Dask, Dash, Jupyter notebooks

• Model versioning and registry tool: Mlflow

• Gitlab / Kubernetes for CI/CD

• Prometheus/Grafana and Kibana for operations

Your profile

• You have at least 2 or 3 years of experience working in Data Science, Applied Mathematics, Computer Science or similar field

• You have worked on at least one deep learning framework such as tensorflow or pytorch

• You have a pragmatic approach to ML where testing and frequent deliveries of small incremental gains supported by validation / alerting processes to avoid regression is preferred to a long tunneled research process

• You're passionate about addressing business challenges through innovative technological solutions

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

• Experience modelling time series and/or price elasticity is a plus

By joining us, you will integrate:

• A self-financed startup with a strong tech identity

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

• Attractive remuneration indexed on performance

• 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:

• A hybrid policy: 2 days of remote work per week and the possibility to work occasionally from abroad

• 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. An initial discussion with our Talent Acquisition Manager

2. An interview with the Hiring Manager

3. A technical test or case study to be prepared

4. A last interview at our offices to discuss your technical test or case study and meet with members of the team

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