- Build, iterate and optimize models and experiment to build state-of-the-art performance within wearable devices 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. 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
- Strong experience in machine learning, signal processing, and software development
- Experience in building and deploying models that can accurately make inference from high-frequency sensor signals
- Programming skills in languages such as Python
- Familiarity with signal processing techniques and libraries such as NumPy, SciPy, and Pandas
- Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly
- Experience with ML frameworks such as TensorFlow, PyTorch, or Keras
- Understanding of machine learning concepts, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction
- Proven experience of working prioritizing and working efficiently, to drive the most impactful work forwards
- Proven experience with Machine Learning model pipeline, topology and architecture, hyper-parameter optimization
- Experience with wearable device data, or other low-level sensor data sources
- Experience with transfer learning, domain adaptation, and few-shot learning
- Familiarity with Explainable AI techniques and libraries such as LIME, SHAP, or TreeExplainer
- Certification in machine learning, data science, or a related field
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