We are seeking a highly skilled Machine Learning (ML) Engineer to join our team in developing cutting-edge algorithms for health & wellness. In this role, you will be working with research scientists and engineers and apply your expertise in machine learning, signal processing, and software development to build and deploy models that can accurately detect and predict various health conditions.

MINIMUM QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Master’s or Ph.D. degree in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field
• 3+ years of experience in machine learning, signal processing, and software development
• Demonstrated experiences in delivering software implementations in languages such as Python
• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras
• Familiarity with signal processing techniques and libraries such as NumPy, SciPy, and Pandas
• Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly
• Familiarity with machine learning concepts, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction
• Evidenced success with problem-solving skills, attention to detail, and working on time-sensitive projects

PREFERRED QUALIFICATIONS:

• Experience with wearable device data, or other relevant healthcare data sources
• Knowledge of biosignals, and applications including sleep, sleep apnea, Heart Rate Variability, and other health-related topics
• Experience with transfer learning, domain adaptation, and few-shot learning
• Familiarity with Explainable AI (XAI) techniques and libraries such as LIME, SHAP, or TreeExplainer

• Build, iterate and optimize ML models and experiment to build state-of-the art performance within wearables constraints
• Develop and implement data augmentation techniques to improve model performance and robustness
• Collaborate with cross-functional teams, including hardware engineers, software developers, and clinical experts, to integrate ML models into existing systems and products
• Evaluate and optimize model performance using various metrics and techniques, such as accuracy, precision, recall, F1 score, and ROC-AUC
• Stay up-to-date with the latest advancements in machine learning, signal processing, and related fields, and apply this knowledge to improve model performance and innovation
• Document and communicate technical findings and results to both technical and non-technical stakeholders
• Participate in code reviews and contribute to the development of best practices for ML engineering within the company