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Machine Learning Engineer & Ops

CDI
Paris
Salaire : Non spécifié
Début : 03 avril 2022
Télétravail fréquent
Expérience : > 2 ans
Éducation : Bac +5 / Master

Mindee
Mindee

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Le poste

Descriptif du poste

As a ML Engineer you will be part of the datascience team in close collaboration with the data scientists. Your focus will be on the following tasks :

  • Enforce the production of reliable and efficient code into a production setting.
  • Improve the efficiency of the code, either algorithmically or by porting python code to rust.
  • Implement multiple new tools to improve the efficiency of the team, such as experiment automation and distributed computation.
  • When you are not to busy with that, you will have the occasion to help the data scientists to produce new models and pipelines !

Profil recherché

What you need to succeed:

  • A master degree from a top-tier university with computer science components;

  • A strong background in computer science;

  • Proficiency with Python;

  • Ability to produce clean and usable code in a collaborative setting. That means you are great at Git !;

  • A recent deep learning framework, such as Pytorch or Tensorflow;

  • Fluency in english;

  • You are autonomous and comfortable with team-work;

  • You are excited to work in a fast-paced growing environment;

  • You describe yourself as a swiss army knife, that means you love working on many different topics and learning new things !

  • You are curious about state-of-the-art deep learning models;

Additional Desirable Qualifications:

  • You have a strong knowledge of one of these languages: C / C++ / Rust;

Déroulement des entretiens

  • A first interview with the CSO and Head of Science
  • A technical assessment consisting of :

    • Coding exercise: Improve a Python algorithmic coding exercise (at home or live, you choice)
    • Discussion around a technical aspect of some ML project you worked before
  • Coffee and informal discussions with other team members at Mindee

Overall, this can be done in one or two weeks, depending on your disponibilities.

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