Senior/Staff Machine Learning Engineer (Model Dev) — Artera

Artera is hiring a Senior/Staff Machine Learning Engineer for model development on a remote-US basis, with the role owning AI biomarker development end to end from problem framing through regulatory submission and production deployment. The posting does not publish pay, while 317 of the 409 Staff AI-engineering roles listed on the same board publish salaries with a middle half of $228k–$290k and a median of $256k. Artera describes itself as an artificial intelligence company developing foundation models that analyze clinical and pathology data to guide cancer therapy selection.

Senior/Staff Machine Learning Engineer Model Dev At a glance - Salary - Not published - Location - Remote-US - Work type - Remote - Level - Staff - Posted - today - Verified live - today How the pay compares This posting doesn't publish pay. 317 of the 409 Staff AI-engineering roles in the United States https://frontierroles.com/ on this board do: the middle half pay $228k–$290k , with a median of $256k . Middle half of the 317 that publish payMedian10th–90th percentileAnnual, USD Apply on company site opens in new tab https://jobs.lever.co/artera/5263bd60-3a9c-4686-ba86-2a00201c0cf0/apply Job description About Us: Artera is an artificial intelligence company dedicated to transforming cancer care. We’ve developed foundation models that analyze clinical and pathology data, generating actionable insights that guide therapy selection and improve outcomes for cancer patients. By continuously improving these models, we aim to uncover the biological mechanisms driving cancer progression. We're looking for an experienced machine learning engineer to own 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. Beyond owning a biomarker program, you'll take on the hardest cross-cutting problems in our field: robustness across scanners and sites, mechanistic interpretability of model decisions, and the next generation of our pathology foundation models.