Senior/Staff Machine Learning Engineer (Model Dev)

Artera is an artificial intelligence company transforming cancer care through foundation models that analyze clinical and pathology data to generate actionable treatment insights. The Senior/Staff Machine Learning Engineer will lead end-to-end AI biomarker development, advance pathology foundation models, ensure robustness and interpretability, and support regulatory submission and production deployment.

Responsibilities:

• Lead the technical effort and define the strategic vision for patient-facing products, in partnership with product, biostatistics, clinical development, and regulatory/quality
• Design and build AI-based biomarkers on multimodal data including whole-slide images, clinical variables, and molecular data to predict patient outcomes, treatment benefit, and molecular traits
• Advance our core self-supervised foundation models and the downstream architectures built on them (multiple-instance learning, time-to-event / hazard models, segmentation and classification components), with generalization as a first-order objective
• Own score reproducibility across scanners, institutions, staining protocols, and patient populations
• Develop and integrate mechanistic interpretability methods to explain model decisions, build clinician trust, and drive actionable model improvements
• Architect tools and processes that streamline the end-to-end model development lifecycle from prototyping through production deployment and monitoring ensuring efficiency, reproducibility, regulatory compliance, and scale
• Author and defend regulatory and quality documentation, and represent AI in design and development reviews
• Plan and manage delivery: break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risk early, and hold submission and launch dates
• Publish in peer-reviewed journals and present at clinical and ML venues; support external academic and industry collaborations
• Mentor and coach machine-learning scientists and engineers, fostering their technical growth and collaboration skills, and raise the bar on scientific rigor, code quality, and written communication across the team