AI/ML Engineer

Accountabilities:

• • Design, develop, train, and evaluate supervised and unsupervised machine learning models using longitudinal and real-world health data.
  • Build scalable and reliable data pipelines supporting model training, testing, validation, and deployment.
  • Develop machine learning solutions capable of identifying health risks, supporting personalized interventions, and enabling adaptive care pathways.
  • Work closely with software and engineering teams to integrate ML models into production Software as a Medical Device (SaMD) and digital therapeutic solutions.
  • Establish robust processes for model reproducibility, monitoring, evaluation, and performance optimization.
  • Ensure models and ML workflows meet relevant requirements around explainability, reliability, privacy, and regulatory compliance.
  • Apply appropriate techniques for real-world signal processing, data preparation, feature engineering, and model validation.
  • Contribute to the evaluation and responsible adoption of generative AI technologies for healthcare education, coaching, and related applications.
  • Collaborate with cross-functional experts to translate clinical and behavioral health challenges into effective technical solutions.
  • Contribute to the continuous improvement of machine learning infrastructure, development practices, and MLOps processes.

  Requirements:

  • 4+ years of professional experience in AI/ML engineering, machine learning, data science, or a closely related field, ideally involving digital health or healthcare datasets.
  • Strong proficiency in Python and hands-on experience with data wrangling, machine learning development, and real-world signal processing.
  • Practical experience with leading ML frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
  • Experience designing and implementing MLOps pipelines for reliable model development, testing, deployment, and maintenance.
  • Understanding of machine learning applied to longitudinal, real-world, or time-series health data.
  • Familiarity with clinical validation, model evaluation, bias mitigation, and privacy-preserving machine learning practices.
  • Understanding of the challenges associated with deploying AI/ML solutions in regulated healthcare or Software as a Medical Device environments.
  • Strong analytical and problem-solving abilities, with a rigorous and evidence-driven approach to technical decisions.
  • Ability to work collaboratively with engineering, data science, healthcare, and other multidisciplinary stakeholders.
  • Strong communication skills and the ability to explain complex technical concepts clearly to both technical and non-technical audiences.
  • Interest in generative AI and its responsible application to healthcare education, coaching, and patient support is a plus.
  • Experience working effectively in a remote or distributed environment is preferred.

  Benefits:

  • Fully remote working environment, with Europe-based candidates preferred.
  • Full-time, permanent position within a technology-focused healthcare environment.
  • Opportunity to contribute to the development of next-generation digital health and digital therapeutic solutions.
  • Work with real-world health data and advanced AI/ML technologies focused on improving patient outcomes.
  • Exposure to a certified Software as a Medical Device environment with strong emphasis on evidence, quality, and regulatory standards.
  • Collaboration with multidisciplinary teams and leading partners across Pharma, MedTech, and academic research.
  • Opportunity to work at the intersection of artificial intelligence, behavioral science, digital therapeutics, and healthcare.
  • Meaningful opportunity to contribute to scalable solutions addressing chronic care management.
  • Remote-first culture supported by collaborative teams focused on measurable patient impact.