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
Develop and evaluate AI-based biomarkers using multimodal data, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular traits. Contribute to the development and evaluation of self-supervised foundation models and downstream machine-learning models, including multiple-instance learning, time-to-event / hazard models, segmentation, and classification. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 1+ years of experience developing machine-learning or deep-learning models using PyTorch (or TensorFlow), including relevant master's or graduate research experience
- Familiarity with oncology and biomarker development, including cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable
- Experience working with real-world datasets and evaluating machine-learning models using appropriate metrics and validation approaches
- Strong Python programming skills and familiarity with modern software-development practices, including version control, testing, and code review
- Ability to analyze experimental results, troubleshoot model behavior, and communicate findings clearly
- Ability to collaborate effectively with ML engineers, scientists, and cross-functional partners
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 a machine learning engineer to help develop AI biomarkers that improve cancer care. You'll work closely with experienced ML scientists and engineers, as well as clinical, biostatistics, product, and regulatory partners, across the model-development lifecycle, from prototyping and experimentation through validation and production deployment. You'll contribute to challenging problems in medical AI, including building models from digital pathology and clinical data, improving robustness across scanners and sites, understanding model behavior, and advancing our pathology foundation models.
Develop and evaluate AI-based biomarkers using multimodal data, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular...
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