Essential Responsibilities:
- Lead the technical effort and define the strategic vision for developing novel biomarkers in collaboration with partners from product, clinical development, and biostatistics.
- Design and build AI-based biomarkers on multimodal data (including whole-slide images) to predict molecular traits and patient outcomes; and evaluate emerging technologies to continuously enhance product capabilities.
- Architect and implement tools and processes to streamline the end-to-end model development lifecycle—from prototyping to production—ensuring efficiency, robust performance, regulatory compliance, and scalability.
- Develop and integrate mechanistic interpretability methods to explain model decisions, build customer trust, and drive actionable improvements.
- Drive and contribute to publications in scientific journals and presentations at machine-learning conferences.
- Mentor and coach a team of machine-learning scientists and engineers, fostering their technical growth and collaboration skills.
Experience Requirements:
- 5+ years of industry experience using PyTorch or TensorFlow.
- 2+ years of experience as a technical lead, launching and monitoring machine-learning products in production environments.
- Proven ability to communicate complex ML concepts effectively to cross-functional, non-ML collaborators.
Desired:
- Track record of research contributions, including peer-reviewed publications and conference presentations.
- History of external academic or industry collaborations.
- Experience with self-supervised representation learning (e.g., MoCo, DINOv2).