Remote ML Engineer — Biosignals for Brain Health AI

Beaconbiosignals

Paris

Sur place

EUR 90 000 - 130 000

Plein temps

14 jours+
Générateur de candidature

Une candidature sur mesure pour ce poste — un CV et une lettre de motivation personnalisés qui correspondent à l’offre.

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Résumé du poste

Beacon Biosignals is seeking a Machine Learning engineer to own the biosignal-based algorithm development lifecycle for medical devices, from data curation to production deployment.

You will apply deep learning and traditional methods as appropriate, advance internal tools, and ensure code quality with tests, CI, and thorough documentation. Collaboration with clients and stakeholders is essential in this role.

Qualifications

  • 4+ years in ML/DL in health sciences or regulated fields.
  • Experience with DSP and statistics; know when not to use ML.
  • Proficient in PyTorch or similar frameworks for training and deployment.
  • Familiar with Transformer/ViT and large-scale modeling.
  • Experience with software engineering practices: tests, version control, code reviews, documentation, CI/CD, experiment tracking.
  • Familiar with biosignals or time-series data or eagerness to learn.
  • Strong communication and teamwork.

Responsabilités

  • Lead the biosignal-based algorithm development lifecycle for medical devices.
  • Select and apply appropriate methods; know when to use DL vs other methods.
  • Enhance internal ML tools, architectures, and reusable code.
  • Ensure algorithms are well-documented, tested, with CI and non-regression tests.
  • Present results to stakeholders and support client engagement.
  • Support client-facing projects and deploy algorithms.

Connaissances

Team collaboration
Communication
Problem solving
Regulatory awareness

Outils

PyTorch
Docker
CI/CD
Git
DSP toolkits

Description du poste

Beacon Biosignals is seeking a Machine Learning engineer to own the biosignal-based algorithm development lifecycle for medical devices, from data curation to production deployment.

You will apply deep learning and traditional methods as appropriate, advance internal tools, and ensure code quality with tests, CI, and thorough documentation. Collaboration with clients and stakeholders is essential in this role.

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