Senior Machine Learning Engineer

At Wiremind, our goal since we started in 2014 is 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 modern technologies (Deep Learning) to process vast quantities of data.

Every day, we handle a broad range of problems from forecasting the demand for railway passengers, calculating the optimal way to fill an aircraft pallet with boxes of multiple dimensions, 3D modelisations, etc.

Our applications are used daily by hundreds of users among the largest players of each industry (railway companies, airlines, etc.) in many countries and several continents. We are now a team of 30+, and growing about +100% every 18 months.
Our business model is built on “software-as-a-service” solutions licensed through long-term contracts, allowing our growth to be based on strong profitability – without requiring any fundraising.

CONTEXT

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 modelisation of the unconstrained demand using ML models (deep learning, LGBM, …) trained on historical data in the form of time-series
• under-constraints optimizations solved using linear programming techniques

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

In this context, Wiremind is now looking for a senior ML engineer capable of making necessary evolution on this existing framework and processes while developing and maintaining scalable ML pipelines.

WHAT YOU WILL DO
In a team shaped to have all profiles necessary to constitute an autonomous departement (devops, software eng., data eng., IA, Operational research), you will be responsible for :

• Participating to the maintenance, development and research of new ML models through reproducible, well documented and versioned pipelines
• Leading the evolution of our general ML training framework and its components (A/B testing, validation, feature extraction, training…)
• Mentoring junior team member and onboard them in the framework
• Ensuring code quality, factorization and documentation
• Having a role in operations by constructing and taking charge of the alerting and monitoring system for algorithms currently in production.

TECHNICAL STACK:
Python 3.7+
Kubeflow over an auto-scaled kubernetes cluster for orchestration
Druid as datastore
Common ML librairies (tensorflow, lgbm, pandas, dash…)
Gitlab for continuous delivery

WHAT IS IMPORTANT TO US

• Strong computer-science background in python with an interest for code quality and good practices (unittesting, pep8, typing)
• A first relevant experience in a data team with production ready pipelines using DAGs
• Knowledge about at least one deep learning framework: tensorflow or pytorch
• 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