Machine Learning Engineer (Model Dev)

Accountabilities: Develop and evaluate AI-based biomarkers using multimodal datasets, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular characteristics. Contribute to self-supervised foundation models and downstream machine-learning systems, including multiple-instance learning, survival and hazard models, segmentation, and classification. Design and evaluate approaches that improve model robustness and reproducibility across scanners, institutions, staining protocols, and patient populations. Investigate model interpretability techniques to explain predictions, strengthen clinician trust, and identify opportunities for model improvement. Build and enhance tools, workflows, and pipelines that enable efficient, reproducible experimentation, validation, and deployment of machine-learning models. Conduct rigorous model evaluation, analyze experimental results, troubleshoot model behavior, and communicate technical findings clearly. Collaborate with ML scientists and engineers alongside product, biostatistics, clinical development, regulatory, and quality teams throughout model development and validation. Support regulatory and quality documentation associated with AI model development, evaluation, and validation. Contribute to peer-reviewed research, conference presentations, and collaborations with academic and industry partners. Requirements At least 1 year of experience developing machine-learning or deep-learning models using PyTorch, TensorFlow, or comparable frameworks, including relevant master's-level or graduate research experience. Familiarity with oncology and biomarker development, including cancer biology, treatment pathways, clinical endpoints, risk stratification, and the characteristics of clinically actionable biomarkers. Experience working with real-world datasets and applying appropriate metrics and validation methodologies to evaluate machine-learning models. Strong Python programming skills and familiarity with modern software-engineering practices, including version control, testing, code review, and maintainable development workflows. Ability to analyze experimental results, investigate model behavior, troubleshoot technical issues, and communicate conclusions effectively. Strong collaboration skills and the ability to work effectively with machine-learning engineers, scientists, clinicians, and other cross-functional stakeholders. Experience with complex clinical datasets such as medical imaging, multi-omics, longitudinal patient records, clinical studies, or multi-institutional cohorts is highly valuable. Familiarity with weakly supervised learning, multiple-instance learning, survival analysis, or related machine-learning techniques is preferred. Experience with self-supervised representation learning, foundation models, or large-scale model development is advantageous. Understanding of dataset shift and variability across sites, devices, scanners, staining methods, or acquisition protocols is a plus. Exposure to machine learning in regulated healthcare environments, including SaMD, FDA 510(k) or De Novo pathways, design controls, or CLIA/LDT validation, is desirable. Research experience demonstrated through publications, conference presentations, internships, or academic projects is valued. Familiarity with cloud-based ML development, distributed training, workflow orchestration, experiment tracking, or reproducible ML pipelines is a plus. Benefits Base salary of $140,000–$180,000 per year , depending on experience, qualifications, and other relevant factors. Equity as a core component of the overall compensation package. 401(k) plan with employer matching. Unlimited paid time off (PTO). Remote work environment. Opportunity to work on challenging medical-AI problems with potential to improve cancer diagnosis, treatment decisions, and patient outcomes. Collaboration with experienced ML scientists and engineers as well as clinical, biostatistics, product, and regulatory professionals. Opportunities to contribute to publications, conference presentations, and external academic or industry collaborations. Inclusive and equal-opportunity workplace committed to bringing together diverse perspectives and backgrounds.