Machine Learning Engineer – Wearable Health Algorithms

all.health; London; Remote; GBP 50,000 - 90,000; Full time

Job summary

An innovative firm is seeking a Machine Learning Engineer to develop impactful healthcare solutions using wearable data. In this high-impact role, you will design FDA-compliant algorithms that analyze physiological signals from wearables, shaping the future of digital health. Collaborate with diverse teams to ensure regulatory compliance while optimizing algorithms for deployment on resource-constrained devices. This is a unique opportunity to contribute to clinically validated ML solutions that can transform patient care and enhance health outcomes. Join a forward-thinking company and make a significant impact in the healthcare industry.

Qualifications

• 3-5+ years experience applying ML to time-series or physiological data.
• Strong foundation in signal processing and time-series modeling.

Responsibilities

• Design and implement ML models for real-time analysis of wearable data.
• Collaborate with teams to ensure solutions meet FDA and regulatory standards.

Skills

Machine Learning

Signal Processing

Time-Series Modeling

Python

Deep Learning

Anomaly Detection

Education

MS in Machine Learning

PhD in Biomedical Engineering

Bachelor in Computer Science

Tools

PyTorch

TensorFlow

MLOps

Job description

all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer.

Job Summary:

We’re looking for a Machine Learning Engineer with a passion for developing impactful healthcare solutions using wearable data. You’ll play a key role in building real-time, FDA-compliant algorithms that analyze continuous physiological signals from wearables. This is a high-impact role with the opportunity to shape the future of digital health and help bring clinically validated, regulatory-ready ML solutions to market.

Location: Remote / Hybrid / [USA-SF, USA-remote, UK-London, UK-remote]

Responsibilities:

1. Design and implement machine learning models for real-time analysis of wearable biosignal data (e.g., ECG, PPG, accelerometer).
2. Develop algorithms that meet clinical-grade performance standards for use in regulated environments.
3. Preprocess and manage large-scale, continuous time-series datasets from wearable sensors.
4. Collaborate with clinical, product, and regulatory teams to ensure solutions align with FDA, SaMD, and GMLP requirements.
5. Optimize algorithms for deployment on resource-constrained devices (e.g., edge, mobile, embedded systems).
6. Run thorough validation experiments including performance metrics like sensitivity, specificity, ROC-AUC, and precision-recall.
7. Contribute to technical documentation and regulatory 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-series or physiological data.
3. Strong foundation in signal processing and time-series modeling (e.g., deep learning, classical ML, anomaly detection).
4. Proficient in Python and ML frameworks such as PyTorch or TensorFlow.
5. Familiarity with FDA regulatory pathways for medical software (e.g., 510(k), De Novo), and standards like IEC 62304 or ISO 13485.
6. Experience with MLOps practices and model versioning in compliant environments.

Preferred Qualifications:

1. Experience building ML models with wearable data (e.g., continuous heart rate, motion, respiration).
2. Exposure to embedded AI or edge model deployment (e.g., TensorFlow Lite, Core ML, ONNX).
3. Knowledge of healthcare data privacy and security (e.g., HIPAA, GDPR).
4. Familiarity with GMLP (Good Machine Learning Practice) and clinical evaluation frameworks.

The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.