Senior/Staff Machine Learning Engineer (Model Dev)

About the role

Artera is an artificial intelligence company dedicated to transforming cancer care. We develop foundation models that analyze clinical and pathology data, generating actionable insights that guide therapy selection and improve outcomes for cancer patients. The role involves owning AI biomarker development end‑to‑end—from problem framing with clinical and biostatistics partners, through model development and validation, to regulatory submission and production deployment. You will also tackle cross‑cutting challenges such as robustness across scanners and sites, mechanistic interpretability of model decisions, and advancing our pathology foundation models.

Responsibilities

• Lead the technical effort and define the strategic vision for patient‑facing products, partnering with product, biostatistics, clinical development, and regulatory/quality teams.
• Design and build AI‑based biomarkers on multimodal data (whole‑slide images, clinical variables, molecular data) to predict patient outcomes, treatment benefit, and molecular traits.
• Advance core self‑supervised foundation models and downstream architectures (multiple‑instance learning, time‑to‑event/hazard models, segmentation, classification) with generalization as a primary objective.
• Ensure 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 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; 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 meet 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 technical growth, collaboration skills, scientific rigor, code quality, and written communication.

Experience Requirements

• 5+ years of industry experience building deep learning systems in PyTorch or TensorFlow.
• 2+ years as a technical lead, launching and monitoring machine‑learning products in production environments.
• Demonstrated depth in oncology and biomarker development, including familiarity with cancer biology, treatment pathways, clinical endpoints, risk stratification, and clinical actionability of biomarkers.
• Proven project‑management ability to scope, sequence, and manage dependencies and risk across multiple teams on dated deliverables.
• Strong communication skills for conveying complex ML concepts to cross‑functional, non‑ML collaborators.
• Experience mentoring or managing ML scientists and engineers.

Desired Experience

• Building ML on complex clinical data such as medical imaging, multi‑omics, or longitudinal patient records, including weakly supervised learning and handling variation across sites, devices, and protocols.
• Developing ML in regulated environments (FDA 510(k)/De Novo, CE/UKCA, SaMD, design controls, or CLIA/LDT validation).
• Experience with self‑supervised representation learning (e.g., DINOv3) and adapting medical foundation models to downstream clinical tasks.
• Working with data from randomized controlled trials and multi‑institutional clinical cohorts.
• Peer‑reviewed publications and conference presentations; history of external academic or industry collaborations.
• Cloud‑scale training and workflow orchestration (e.g., Flyte/Union, Kubernetes, AWS), experiment tracking, and reproducible ML pipelines.