Our ML engineers work on a diverse range of projects, including: prototyping new models, providing software design and support to scientists, implementing tools and libraries, as well as working closely with our software engineering team to scale the modeling side of our products.

Examples of responsibilities:

  • Work on the ML side of our product portfolio, in collaboration with our
    project and product managers: automate and optimize data pipelines, improve
    models used in production to be more accurate or more efficient, etc.
  • Use and develop machine learning and deep learning algorithms to solve
    applied problems in various disciplines such as Computer Vision, Time
    Series Analysis or Natural Language Processing.
  • Present and communicate project progress and development clearly, in
    written and oral forms
  • Mentor more junior members of our team, to help them grow technically
    and professionally.

Minimum qualifications:

  • BSc/BEng in computer science or related technical field or equivalent
    practical experience (MSc/MEng or PhD preferable
  • Proven experience in python for scientific/statistical use (3+ years)
  • Know how to work in a team, and familiar with modern software engineering practices such as testing, continuous integration, source code management
  • Able to take initiative, doesn't require constant supervision, very organized and methodological in his/her approach
  • Proven experience leading ML-based projects with small (2-3 people) teams

The ideal candidate would have one or more of the following qualifications:

  • Working knowledge of C or C++
  • Background in image processing or NLP would be a plus
  • Proven experience in TensorFlow, and able to implement the latest research in the framework.
  • Familiar with the scientific python ecosystem (NumPy, Pandas, Jupyter notebooks, Scikit Learn)

We are working in a dynamic environment, using modern technologies such as TensorFlow, PyTorch, Docker, Google Cloud Platform and Kubernetes. Some of the MLE team members have significantly contributed to open source projects such as NumPy, Scipy, Scikit Learn, etc.