Senior Machine Learning Engineer

Responsibilities

• Improve existing production models through systematic error analysis, better data, targeted experiments, and changes to model architecture and training.
• Develop models for new products, taking problems from initial formulation and feasibility experiments through training, validation, and production integration.
• Partner with clinicians and product colleagues to define meaningful evaluation criteria, including sensitivity, specificity, and the clinical consequences of different error types.
• Evaluate robustness across patient populations, clinical sites, imaging equipment, and acquisition conditions; identify performance gaps and build evidence that improvements generalize.
• Improve data curation and annotation workflows, including coverage gaps, label quality, and prevention of data leakage.
• Build reproducible training and evaluation pipelines with traceable datasets, experiments, and model versions.
• Partner with software engineers to optimize inference speed, resource use, and operational reliability, and investigate model issues that emerge in production.
• Review relevant research, test promising approaches, and make evidence-based decisions about what to adopt.
• Contribute to validation and technical documentation with quality and regulatory colleagues.
• Review code and experiments, mentor colleagues, and communicate findings and trade-offs clearly.

Languages