Lead AI Biomarker Engineer for Oncology Foundations

StartX; United States; Hybride; USD 180 000 - 240 000; Plein temps

Avantages offerts par ce poste

Equity in compensation

401k matching

Unlimited PTO

Résumé du poste

Artera is an AI company shaping cancer care by building foundation models that analyze clinical and pathology data to guide therapy choices. We are seeking an experienced ML engineer to own biomarker development end to end—from clinical framing with partners to production deployment and regulatory submission.

You will tackle cross-cutting challenges in robustness, interpretability, and scalable AI for pathology foundation models.

Qualifications

• 5+ years of industry experience building deep learning systems in PyTorch (or TensorFlow).
• 2+ years of experience as a technical lead, launching and monitoring ML products in production.
• Demonstrated depth in oncology and biomarker development and clinical endpoints.

Responsabilités

• Lead the technical effort and define the strategic vision for patient-facing products.
• Design and build AI-based biomarkers on multimodal data to predict patient outcomes.
• Advance foundation models and downstream architectures with strong generalization.
• Own score reproducibility across scanners, institutions, and protocols.
• Develop mechanistic interpretability methods to explain model decisions.
• Architect end-to-end model development lifecycle from prototyping to deployment.
• Publish in journals and support external collaborations.
• Mentor ML scientists and engineers to raise technical rigor.

Connaissances

Deep learning

PyTorch

Technical leadership

Project management

Oncology biomarker

Communication

Mentoring

Outils

TensorFlow

Flyte

Kubernetes

AWS

Description du poste

Artera is an AI company shaping cancer care by building foundation models that analyze clinical and pathology data to guide therapy choices. We are seeking an experienced ML engineer to own biomarker development end to end—from clinical framing with partners to production deployment and regulatory submission.

You will tackle cross-cutting challenges in robustness, interpretability, and scalable AI for pathology foundation models.