Senior ML Engineer

  • CDI 
  • Paris
  • Télétravail partiel possible
  • Bac +5 / Master
  • > 2 ans

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    Le poste

    Senior ML Engineer

    • CDI 
    • Paris
    • Télétravail partiel possible
    • Bac +5 / Master
    • > 2 ans



    Since creating Wiremind in 2014, our goal has been to create optimization systems for the transport, logistics, sports and hospitality industries, without compromising on user experience. We build solutions that blend great design with cutting-edge technology (e.g. deep learning, distributed computing) to process vast quantities of data.

    This leads us to work on varied and challenging projects, including:

    • Forecasting demand for railway passengers between two major cities
    • Handling millions of data points collected daily through web-crawling
    • Calculating optimal ways to fill an aircraft with boxes of unpredictable shapes and displaying the result in 3D

    Our applications are used daily by hundreds of users among the largest players of each target industry across many countries and several continents. We are now a team of 40+, growing by ~100% every 18 months.

    Our business model is built on software-as-a-service solutions licensed through long-term contracts, allowing our rapid growth to be based on strong, stable profitability – without requiring any fundraising.

    Job description


    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

    The team is now entering a scaling phase where we will face the challenge to stay agile in terms of innovation while supporting and closely monitoring deployed algorithms. To address this issue, we organize our work around Kubeflow (https://www.kubeflow.org/), a MLOps tool empowering data scientists to focus on high-added value tasks by maintaining a common framework and re-usable, modular components for all team members.

    We have recently entered a partnership with an industrial leader in the sector of airfreight to develop an all-inclusive solution for the industry. This new solution will require a dynamic pricing component able to accurately predict and recommend a price for a shipment entering the network of an airline. In this context, Wiremind is now looking for an experienced ML engineer, capable of working with a rich codebase in order to make the additions necessary to our framework and processes, all the while developing and maintaining scalable ML pipelines.


    You will be joining a team shaped to have all profiles necessary to constitute an autonomous departement (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 senior ML engineer, you will be responsible for :

    • Taking part in the maintenance, development and search for new ML models through reproducible, well-documented and versioned pipelines
    • Improving machine learning R&D at Wiremind (e.g. by taking into account new variables or modelling inherent constraints)
    • Developing monitoring tools to diagnose and improve the existing ML models using dash, plotly, tensorboard.


    • Python 3.7+
    • KubeFlow over an auto-scaled kubernetes cluster for orchestration
    • Druid as datastore
    • Common ML libraries: tensorflow, lgbm, xgboost, pandas, dask, dash
    • Gitlab for continuous delivery

    Preferred experience


    • Strong computer science background in python, with a keen interest for code quality and best practices (unit testing, pep8, typing)
    • Knowledge about at least one major deep learning framework, e.g. tensorflow, pytorch
    • A pragmatic, prod-oriented approach to ML: frequent, incremental gains beat a grand quest for perfection.
    • At least 2 years of experience as a data scientist / ML engineer


    • Hands-on experience with the TensorFlow ecosystem
    • Knowledge of a distributed computing framework such as Dask, Spark
    • Prior exposure to modern MLOps tools and practices: dataset validation, automated model retraining and versioning
    • A first experience in a pricing-related domain
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