Wearable ML Research Engineer – Time-Series & Biosignals

Snapchat, Inc.; Paris; Sur place; EUR 75 000 - 120 000; Plein temps

Avantages offerts par ce poste

Paid parental leave

Medical coverage

Mental health support

Long-term incentive

Résumé du poste

Snap Inc. is seeking a Research Engineer to join the Spectacles BCI Team in Paris. You will advance ML for time-series biosignals from wearable devices and collaborate with global hardware and software teams.

The role focuses on developing ML models for cognitive and physiological decoding, building signal-processing pipelines, and translating results into product insights for stakeholders.

Qualifications

• Master’s degree in machine learning, computer science, biomedical engineering, computational neuroscience or related field.
• 3+ years of research or engineering experience developing algorithms or models for time series, biometric, physiological, wearable, behavioral, or multimodal data.
• Hands-on experience with wearable biometrics products or platforms, such as smartwatches, fitness trackers, health trackers, sports-performance wearables, or similar real-world sensing devices.

Responsabilités

• Conduct focused, product-related research projects, defining hypotheses and designing experiments.
• Build signal-processing and ML pipelines for noisy, real-world time-series data.
• Develop algorithms and ML models for cognitive and physiological decoding using multimodal biological signals captured via wearable devices.
• Collaborate with software and research teams on model development and evaluation, data capture and demos.
• Translate findings into clear recommendations for stakeholders and leadership.

Connaissances

Signal processing

Python

Multivariate time series ML

Communication

AI-assisted workflows

Formation

Master’s degree in ML/CS/biomedical engineering

PhD in related field

Description du poste

Snap Inc. is seeking a Research Engineer to join the Spectacles BCI Team in Paris. You will advance ML for time-series biosignals from wearable devices and collaborate with global hardware and software teams.

The role focuses on developing ML models for cognitive and physiological decoding, building signal-processing pipelines, and translating results into product insights for stakeholders.