Job Opportunities API

The Public Ledger of Openings

← Back to the ledger

AI/ML Engineer

newelhealth
Companynewelhealth
CategoryEngineering
Location
Remote
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
First seen27 Jul 2026 (the employer did not state a posting date)
Last verified12 Aug 2026
SourceEmployer ATS (bamboohr)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
AI/ML Engineer Location: Remote (Europe-based preferred) Type: Full-time Industry: Digital Health, SaMD, DTx Reports to: Head of Data Science Position Overview Newel Health is looking for an AI/ML Engineer to develop intelligent models that personalize digital therapies. You will work with real-world data from our SaMDs to design machine learning pipelines that predict health risks, optimize interventions, and enable adaptive care pathways on our H.Core platform. Key Responsibilities • Design and train supervised and unsupervised learning models on longitudinal health data. • Build scalable data pipelines for model training, testing, and deployment. • Work closely with engineering to embed models into production SaMDs. • Ensure models meet explainability, reproducibility, and regulatory standards. • Evaluate generative AI tools for education and coaching applications. Required Qualifications • 4+ years in ML/AI engineering, preferably with digital health datasets. • Experience with TensorFlow, PyTorch, scikit-learn, and MLOps pipelines. • Strong skills in Python, data wrangling, and real-world signal processing. • Understanding of clinical validation, bias mitigation, and privacy-preserving ML. Why Join Newel Health • Shape the next generation of digital health solutions. • Work in a certified SaMD environment at the forefront of behavioral science and AI. • Collaborate with leading partners in Pharma, MedTech, and academic research. • Enjoy a remote-first culture, supported by cross-disciplinary teams passionate about patient outcomes. • Be part of an organization building scalable, evidence-based impact in chronic care management.