Senior ML Engineer leading advanced medical data projects, building scalable ML pipelines on AWS, and deploying deep learning models for clinical insights.
About the role
Key Responsibilities
Design, develop, and maintain end‑to‑end machine learning pipelines for large biomedical datasets.
Collaborate with data scientists and clinicians to translate research questions into production‑ready models.
Implement scalable solutions on AWS (S3, SageMaker, Lambda, Glue) ensuring high availability and security.
Optimize model performance using advanced techniques in deep learning and feature engineering.
Document model lifecycle, conduct A/B testing, and monitor model drift in production.
Requirements
5+ years of experience in machine learning engineering, preferably in healthcare or life sciences.
Proficiency in Python, TensorFlow/PyTorch, and SQL.
Hands‑on experience with AWS services for ML (SageMaker, EC2, S3, IAM).
Strong background in data engineering, ETL pipelines, and data governance.
Excellent communication skills and ability to work cross‑functionally with research teams.