artera - Machine Learning Engineer (Model Dev)
Requirements
• 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. • Experience working with complex clinical datasets, such as medical imaging, multi-omics, longitudinal patient records, or data from clinical studies and multi-institutional cohorts. • Familiarity with weakly supervised learning, multiple-instance learning, survival analysis, or related methods. • Experience with self-supervised representation learning or foundation models. • Familiarity with dataset shift and variation across sites, devices, scanners, or acquisition protocols. • Exposure to machine learning in regulated healthcare environments, including SaMD, FDA 510(k)/De Novo, design controls, or CLIA/LDT validation. • Research experience through publications, conference presentations, internships, or academic projects. • Familiarity with cloud-based ML development, including distributed training, workflow orchestration, experiment tracking, or reproducible pipelines.
Responsibilities
• 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. • Develop and evaluate methods to improve model robustness and reproducibility across scanners, institutions, staining protocols, and patient populations. • Explore and apply interpretability methods to explain model decisions, build clinician trust, and drive actionable model improvements. • Build and improve tools and workflows that support efficient, reproducible model development, experimentation, validation, and deployment. • Perform rigorous model evaluation and analysis, communicate findings clearly, and document experiments and technical decisions. • Collaborate with ML scientists and engineers as well as product, biostatistics, clinical development, and regulatory/quality partners throughout model development and validation. • Support regulatory and quality documentation related to AI model development and validation. • Contribute to peer-reviewed publications, conference presentations, and external academic or industry collaborations.
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