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Senior Machine Learning Engineer
On-siteFull-timeAI & Machine LearningHealthcare Technology
Join a health technology company developing predictive intelligence for cardiovascular risk, user engagement, and personalized health recommendations. Own machine learning models from research and data exploration through production deployment and monitoring.
Responsibilities
- Lead end-to-end development of predictive machine learning models across engagement and clinical risk domains.
- Explore data, engineer features, train and validate models, and manage deployment and ongoing monitoring.
- Design models using statistical methods, feature selection, uncertainty quantification, and result interpretation.
- Write clean, tested, maintainable, and scalable production Python code.
- Use AI coding assistants for development, code review, and documentation.
- Collaborate with product managers, data engineers, and software engineers to develop measurable data-driven solutions.
- Research and implement supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning techniques.
- Contribute to MLOps infrastructure, including model serving, versioning, evaluation pipelines, and monitoring.
- Design and interpret A/B tests and other experiments measuring model, feature, and intervention impact.
Requirements
- 5+ years of experience developing, deploying, and maintaining production machine learning models.
- Bachelor’s degree in statistics, computer science, applied mathematics, engineering, or a related quantitative field.
- Strong expertise in statistics and probability, including inference, hypothesis testing, Bayesian methods, causal inference, and experimental design.
- Strong production software engineering skills in Python, including testing, version control, and reproducibility.
- Proficiency with AI coding assistants in development workflows.
- Expertise with frameworks such as PyTorch, scikit-learn, XGBoost, or LightGBM.
- Experience with end-to-end machine learning pipelines, feature engineering, model registries, and deployment tooling.
- Ability to communicate complex statistical and technical findings to technical and non-technical stakeholders.
Nice to have
- Experience with AWS, Docker, Kubernetes, or MLOps platforms.
- Experience with healthcare or clinical datasets, including wearable, EMR, or claims data.
- Experience with recommendation systems, reinforcement learning, or advanced causal inference.