Machine Learning Engineer – Wearable HealthAlgorithms ...

all.health
London
6 days ago
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all.health is at the forefront of revolutionizinghealthcare for millions of patients worldwide. Combining more than20 years of proprietary wearable technology with clinicallyrelevant signals, all.health connects patients and physicians likenever before with continuous, data-driven dialogue. This uniqueposition of daily directed guidance stands to redefine primary carewhile helping people live happier, healthier, and longer. JobSummary: We’re looking for a Machine Learning Engineer with apassion for developing impactful healthcare solutions usingwearable data. You’ll play a key role in building real-time,FDA-compliant algorithms that analyze continuous physiologicalsignals from wearables. This is a high-impact role with theopportunity to shape the future of digital health and help bringclinically validated, regulatory-ready ML solutions to market.Location: Remote / Hybrid / [USA-SF, USA-remote, UK-London,UK-remote] Responsibilities: 1. Design and implement machinelearning models for real-time analysis of wearable biosignal data(e.g., ECG, PPG, accelerometer). 2. Develop algorithms that meetclinical-grade performance standards for use in regulatedenvironments. 3. Preprocess and manage large-scale, continuoustime-series datasets from wearable sensors. 4. Collaborate withclinical, product, and regulatory teams to ensure solutions alignwith FDA, SaMD, and GMLP requirements. 5. Optimize algorithms fordeployment on resource-constrained devices (e.g., edge, mobile,embedded systems). 6. Run thorough validation experiments includingperformance metrics like sensitivity, specificity, ROC-AUC, andprecision-recall. 7. Contribute to technical documentation andregulatory submissions for medical-grade software.Requirements/Qualifications: 1. MS or PhD in Machine Learning,Biomedical Engineering, Computer Science, or a related field. 2.3–5+ years of experience applying machine learning to time-seriesor physiological data. 3. Strong foundation in signal processingand time-series modeling (e.g., deep learning, classical ML,anomaly detection). 4. Proficient in Python and ML frameworks suchas PyTorch or TensorFlow. 5. Familiarity with FDA regulatorypathways for medical software (e.g., 510(k), De Novo), andstandards like IEC 62304 or ISO 13485. 6. Experience with MLOpspractices and model versioning in compliant environments. PreferredQualifications: 1. Experience building ML models with wearable data(e.g., continuous heart rate, motion, respiration). 2. Exposure toembedded AI or edge model deployment (e.g., TensorFlow Lite, CoreML, ONNX). 3. Knowledge of healthcare data privacy and security(e.g., HIPAA, GDPR). 4. Familiarity with GMLP (Good MachineLearning Practice) and clinical evaluation frameworks. Thesuccessful candidate’s starting pay will be determined based onjob-related skills, experience, qualifications, work location, andmarket conditions. These ranges may be modified in the future.#J-18808-Ljbffr

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