Machine Learning And Signal Processing Engineers

AAVAA is a Montréal-based start-up that is building a listening device that selectively hears what you desire to hear without any cumbersome assumptions. Our solution capitalizes on the novelty of Brain and Biosignal Synthesis, the robustness of Acoustics, and the strength of Artificial Intelligence. Come work alongside passionate entrepreneurs!

Positions are available at different levels, from interns to visiting graduate and postdoc trainees enrolled in a university, to full-time employees.

To apply, please send your cover letter, CV or resume, and contact information for 2 references to

With AAVAA you get:

  • The chance to solve one of the biggest challenges of human health
  • Generous vacation, salary, and benefits program
  • Work in the leading deep tech start-up incubator, TandemLaunch, surrounded by start-ups with specialists who are building the future with you
  • A safe, healthy, and inclusive work environment
  • Healthy snacks, frequent celebrations, endless tea and latte, social outings, and more!


Machine Learning and Signal Processing Engineers

Montréal, Canada   ·    Full time


Engineers with experience in the following areas:

  • Machine learning (e.g., DNN, CNN, RNN, GAN, LSTM and SVM, KNN, Naive Bayes.)
  • Deep learning frameworks (e.g., PyTorch, TensorFlow, Keras, Scikit)
  • Advanced digital signal processing with experience in biosignal analysis
  • High-level programming skills in Python, Matlab, C, C++, Java, etc.
  • Fourier transforms, discrete signals, and filter design
  • Time series data analysis
  • Statistical methods such as Bayesian statistics and statistical inference


Good to have:

  • Advanced acoustics including techniques such as sound source separation, speech enhancement, noise reduction and cancellation, etc.
  • Feature engineering and dimensionality reduction
  • Brain-computer interface and neuroscience
  • Experience with firmware development of ARM based 32 bit microcontrollers
  • Knowledge of using cloud platforms
  • Github

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