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MLOps & DevOps

We are looking for a passionate, dedicated and team-oriented MLOps and DevOps engineer, with prior experience in Computer Vision and ideally, in IoT or embedded systems as well.


Lille / Paris


Full time

What You’ll Do

As a part of our infrastructure team and under the direct supervision of our CTO, you will be responsible for designing, implementing and maintaining automated pipelines to track, train, test and deploy our AI models and Computer Vision solutions, end-to-end. In particular, this includes the following primary responsibilities:

  • Design and implement state-of-the-art MLOps practices and tools to help us train, track, optimize, evaluate and deploy our AI models to production automatically and at scale.

  • Facilitate the delivery of our services and applications by applying and maintaining DevOps practices in our code ecosystem.

  • You are a team-player, with a strong taste for self improvement and sharing technical- and non-technical knowledge with your peers.

  • Remain up-to-date with the most relevant tools and technologies at the bleeding edge.

Who You are

  • Academic degree and background: Master’s degree or Ph.D. in a relevant tech or scientific field, e.g., Computer Science, Machine Learning, Computer Vision, Robotics, IoT, Electrical engineering.

  • Level of experience: 2-5 years of experience in delivering production-ready software is strongly encouraged.

  • Strong basics in software engineering are expected, Object-Oriented Programming and design, big O notation, code optimization and profiling.

  • Fluency in Python 3+ is mandatory.

  • Strong knowledge and experience using Docker and Kubernetes are also a must.

  • Prior successful experience with MLOps and DevOps concepts and tools in terms of CI/CD/CT and orchestration (e.g., Git, Jenkins, Kubernetes ecosystem, Airflow, MLFlow, Argo or other conceptually equivalent stacks).

  • Sound knowledge in theoretical and practical Deep Learning with prior experience with a mainstream Deep Learning framework (e.g., PyTorch, TensorFlow, TensorFlow Lite, PyTorch Lightning).

  • Technical english is mandatory.

  • Other technical nice-to-haves: Neural Network optimization, C++, OpenCV, prior experience with IoT and/or embedded systems.

  • Soft skills and culture fit: Team player, autonomous, goal-oriented, organized and patient. 

Why Join us?

  • We are an ambitious, passionate, fun and talented team made of engineers, researchers and business people with the common goal to bring ethical and autonomous AI to help cities and industries become a better place.

  • We are dreamers and pushing through technical limits and current scientific state-of-the-art is inscribed in our DNA.

  • We have secured a SEED investment funding and have won a major business market.

  • We are lucky enough to receive continual help and feedback from world class advisors in Telecoms, IoT, tech and business.

  • We are also backed by three prestigious start-up acceleration programs, namely, the Alacrity foundation (Lille), Station F (Paris) and the start-up program provided by Ecole des Ponts ParisTech.

  • Our culture is designed to make our workplace fun, flexible and enjoyable to all.

  • We are moms, dads, brothers, sisters, partners, hikers, chess- and sports players that value the time we all need to be better, happier human beings and enjoy life.

What we offer 

  • Contract type: Full-time position (CDI).

  • Location: Paris (at the prestigious Station F) or Lille (downtown), and partly remote.

  • Salary & equity: Competitive package, based on profile and experience.

  • Other benefits and perks: 50% of public transportation fees, 70% health insurance and free coffee!

How to apply?

You can apply by sending an email to careers at or through our online application system at by including (i) a resume (up to date), (ii) a cover letter and (iii) any information that would be deemed useful in assessing the application (e.g., list of prior technical or academic projects, research papers if applicable, reference letters etc)..

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