Machine Learning Engineer


  • Reviewing state-of-the-art machine learning & deep learning technologies for analysis of biosignals.
  • Writing robust data pipelines for:
    • feature engineering and data modelling
    • model hyperparameter optimization
    • model evaluation and explainability
    • model training, deployment and automated retraining
    • model version tracking & governance
    • data archival & version management
    • model and drift monitoring
  • Improving readability and efficiency of the code.
  • Writing documentation, tests and visualizations whenever necessary.


  • MSc or (preferably) PhD in Computer Science, Mathematics, Statistics, Bioinformatics, Biostatistics, Computational Neuroscience or similar.
  • In-depth knowledge of statistics and machine learning concepts. Expertise in signal processing and deep learning is also strongly recommended.
  • Proven experience in machine learning projects, in particular, experience with predictive models based on time-series data from sensors.
  • Good software engineering skills and the ability to productize models.
  • Proficiency in Python and knowledge of machine learning (scientific) libraries/frameworks such as Sklearn, Scipy, Numpy, Pandas, Tensorflow, Pytorch.
  • Interest in human physiology, medicine, and wellness.
  • Scientific track record, i.e. papers published in machine learning or similar - conferences.
  • Participation in Kaggle.

nice to have

  • Knowledge of Matlab and previous experience with analysis of bio-signals (ECG, EEG, PPG).

we offer

  • Opportunity to co-create meaningful technology and products that improve people’s lives.
  • Culture of ownership, openness and trust.
  • Working with professionals in a small dream team.
  • The most effective and proven cooperation methodologies and tools.
  • Freedom and flexibility working remotely or on-site in Wroclaw, Poland.
  • Unlimited, paid vacation time.
  • Private healthcare.
  • Work equipment and tools of your choice.
  • Competitive and fair salary depending on skills and experience.