Manual outreach drains millions in revenue annually. AI Service Squads automate calls, intake, scheduling, and payments across every EHR. Backed by Techstars.
About the role
Lead the technical effort and define the strategic vision for patient-facing products, in partnership with product, biostatistics, clinical development, and regulatory/quality. Design and build AI-based biomarkers on multimodal data — including whole-slide images, clinical variables, and molecular data — to predict patient outcomes, treatment benefit, and molecular traits.
What they're looking for
- 5+ years of industry experience building deep learning systems in PyTorch (or TensorFlow)
- 2+ years of experience as a technical lead, launching and monitoring machine-learning products in production environments
- Demonstrated depth in oncology and biomarker development: familiarity with cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable
- Demonstrated project management ability — scoping, sequencing, and managing dependencies and risk across multiple teams on dated deliverables
- Proven ability to communicate complex ML concepts effectively to cross-functional, non-ML collaborators
- Experience mentoring or managing ML scientists and engineers
More about this role
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.
Lead the technical effort and define the strategic vision for patient-facing products, in partnership with product, biostatistics, clinical development, and regulatory/quality.
Design and build AI-based biomarkers on multimodal data — including whole-slide images, clinical variables, and molecular data — to...
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