Work with us

Machine Learning Engineer

We are looking for a Machine Learning Engineer (MLE) who will work in close collaboration with our ML Scientists in order to implement optimized Machine Learning models ready to be part of production systems. Working in a cross-functional team, you will learn new skills and perform all tasks required to implement end-to-end solutions.

HIKU promotes aggregation and team building activities in order to promote integration, knowledge and dialogue among people.


Continuous training and incentives upon achievement of professional growth goals directed at project development (HI-POC).


Informal and collaborative, geared toward promoting discussion and sharing among individuals and work teams.


Latest generation smartphones and laptops, subscription to Google Drive and Slack to share tasks and project vision.

What we offer

  • You’ll get to work within a young and motivated team;
  • You’ll be using state-of-the-art technology and research on the market, and you’ll be receiving continuous training on both;
  • You’ll have numerous opportunities for personal development;
  • You’ll organize your tasks autonomously, with a focus on results.

What we need to see

  • Bachelor’s degree in Computer Science/Computer Engineering and/or extensive experience in the field;
  • Experience in software development and/or as data scientist and/or data engineer;
  • Excellent knowledge of Python, Scikit-Learn, NumPy, Matplotlib, Pandas, and Unix-based systems;
  • Experience in unit testing, git, continuous integration and containerization;
  • Knowledge of Deep Learning models;
  • Knowledge of TensorFlow or PyTorch;
  • Cloud development experience (GCP or AWS);
  • Open-mindedness for learning new technologies/tools/frameworks;
  • Long-term vision regarding how to advance the data infrastructure to the next level, exploring and evaluating new technologies as appropriate.

Key activities

  • Design and implementation of Machine Learning models;
  • Optimizing the training and inference phase of Machine Learning models on the cloud;
  • Testing of implemented solutions.
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