Machine Learning Engineer; Model Dev

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer (Model Dev) based in United States.

This role offers the opportunity to develop machine-learning solutions with direct impact on cancer care and clinical decision-making. You will work across the full model-development lifecycle, from research and experimentation through validation and production deployment. The position focuses on multimodal clinical and pathology data, including whole-slide images, molecular information, and longitudinal patient data. You will collaborate closely with ML scientists, engineers, clinicians, biostatisticians, product teams, and regulatory partners.

A key focus will be improving model robustness, interpretability, reproducibility, and performance across diverse clinical environments. You will also contribute to foundation-model development, scientific research, publications, and the advancement of medical AI. This is an opportunity to combine strong engineering skills with meaningful scientific and healthcare impact.

• 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.

• At least 1 year of experience developing machine-learning or deep-learning models using PyTorch, Tensor Flow, 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,…