Biomarker ML Engineer — Oncology, Production & Regulatory

Artera; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

401k matching

Unlimited PTO

Job summary

Artera, an AI company transforming cancer care, seeks an experienced ML engineer to own end-to-end biomarker development from framing with clinical partners to production deployment. You will advance multimodal biomarker models, oversee self-supervised foundation models, and ensure robustness across sites and scanners.

Equity and growth opportunities accompany a competitive base salary. Ideal candidates have deep learning expertise, oncology biomarker experience, and a track record of leading ML

Qualifications

• 5+ years of industry experience building deep learning systems in PyTorch (or TensorFlow).
• 2+ years as a technical lead, launching and monitoring ML products in production.
• Deep familiarity with oncology and biomarker development and related endpoints.
• Strong project management across multiple teams and dated deliverables.
• Ability to communicate complex ML concepts to non-ML collaborators.
• Experience mentoring ML scientists and engineers.

Responsibilities

• Lead the technical effort and define strategic vision for patient-facing products with cross-functional partners.
• Design and build AI-based biomarkers on multimodal data to predict outcomes and traits.
• Advance foundation models and downstream architectures with generalization as a priority.
• Ensure score reproducibility across scanners, institutions, and protocols.
• Develop mechanistic interpretability methods to explain model decisions.

Skills

Deep learning

PyTorch

Team leadership

Oncology biomarker

Project management

Communication

Mentorship

Tools

PyTorch

TensorFlow

Flyte

Kubernetes

AWS

Job description

Artera, an AI company transforming cancer care, seeks an experienced ML engineer to own end-to-end biomarker development from framing with clinical partners to production deployment. You will advance multimodal biomarker models, oversee self-supervised foundation models, and ensure robustness across sites and scanners.

Equity and growth opportunities accompany a competitive base salary. Ideal candidates have deep learning expertise, oncology biomarker experience, and a track record of leading ML