Cancer AI ML Engineer: Biomarkers & Foundation Models

Artera Inc.; Northern (KY); On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Equity

401k matching

Unlimited PTO

Job summary

Artera is an AI company focused on transforming cancer care, developing foundation models that analyze clinical and pathology data to guide therapy selection and improve patient outcomes.

We are seeking a machine learning engineer to build AI biomarkers from digital pathology and clinical data, collaborating with ML scientists, clinicians, and regulatory teams across the model development lifecycle from prototyping to production.

Qualifications

• 1+ years of ML/DL model development experience with PyTorch or TensorFlow.
• Strong Python programming and software-development practices.
• Experience with real-world datasets and model evaluation metrics.

Responsibilities

• Develop and evaluate AI biomarkers using multimodal data to predict outcomes.
• Advance self-supervised foundation models and downstream ML models.
• Improve model robustness across scanners, sites, and protocols.
• Apply interpretability methods to explain model decisions.
• Build tools and workflows for reproducible model development and deployment.
• Document experiments and communicate results clearly.
• Collaborate with ML scientists and engineers as well as product, biostatistics, clinical development, and regulatory partners.
• Support regulatory and quality documentation for AI models.
• Contribute to publications and external collaborations.

Skills

PyTorch

TensorFlow

Python

Version control

Tools

Git

Job description

Artera is an AI company focused on transforming cancer care, developing foundation models that analyze clinical and pathology data to guide therapy selection and improve patient outcomes.

We are seeking a machine learning engineer to build AI biomarkers from digital pathology and clinical data, collaborating with ML scientists, clinicians, and regulatory teams across the model development lifecycle from prototyping to production.