Senior Applied ML Algorithm Engineer

Join WHOOP, a leading performance optimization company, as a Senior Applied ML Algorithm Engineer. In this hands-on role, you will develop and deploy signal processing algorithms and machine learning models that transform raw sensor data into accurate physiological insights. You will work closely with various teams to deliver robust on-device algorithms that enhance the member experience while meeting the constraints of wearable hardware.

Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply

About WHOOP

Ability to independently investigate complex algorithmic problems, design rigorous validation experiments, and communicate technical tradeoffs with firmware, hardware, and data science partners

Responsibilities

• Développer et déployer des algorithmes de traitement du signal et des modèles d'apprentissage automatique pour transformer les données brutes des capteurs en informations physiologiques précises et fiables.
• Posséder le développement d'algorithmes depuis l'analyse des données, l'entraînement des modèles, jusqu'à l'implémentation efficace en C/C++.
• Collaborer étroitement avec les équipes de Firmware et de Hardware pour intégrer les algorithmes dans le firmware de production et valider leur exécution sur le matériel cible.
• Demonstrated experience deploying machine learning models to resource-constrained embedded systems, including optimization across accuracy, power, memory, compute, and latency
• Experience developing, training, and evaluating machine learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn
• Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions

Requirements

• MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, or a related quantitative field, or equivalent practical experience
• Proficiency in Python for data analysis, model development, and experimentation, and C/C++ for implementing efficient algorithms in embedded firmware
• 5+ years of experience developing signal processing and machine learning algorithms for time-series or sensor data in real-world applications
• Strong foundation in digital and statistical signal processing for noisy time-series data. Experience with physiological signals or wearable sensors is a plus

Pay

USD 150,000 - 215,000