Machine Learning Engineer

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.

• Develop and evaluate AI-based biomarkers using multimodal datasets, including whole-slide images, clinical variables, and molecular data
• 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 and strengthen clinician trust
• Build and enhance tools, workflows, and pipelines for efficient, reproducible experimentation, validation, and deployment
• Conduct rigorous model evaluation, analyze experimental results, troubleshoot model behavior
• Collaborate with ML scientists, engineers, product, biostatistics, clinical development, regulatory, and quality teams
• Support regulatory and quality documentation associated with AI model development
• Contribute to peer-reviewed research and conference presentations

• At least 1 year of experience developing machine-learning or deep-learning models using PyTorch, TensorFlow, or comparable frameworks
• Familiarity with oncology and biomarker development
• Experience working with real-world datasets and applying appropriate metrics and validation methodologies
• Strong Python programming skills and familiarity with modern software-engineering practices, including version control, testing, code review
• Experience with complex clinical datasets such as medical imaging, multi-omics, longitudinal patient records
• Familiarity with weakly supervised learning, multiple-instance learning, survival analysis
• Experience with self-supervised representation learning, foundation models, or large-scale model development
• Understanding of dataset shift and variability across sites, devices, scanners, staining methods
• Exposure to machine learning in regulated healthcare environments (SaMD, FDA 510(k), De Novo pathways, CLIA/LDT validation)
• Familiarity with cloud-based ML development, distributed training, workflow orchestration, experiment tracking

• Base salary of $140,000–$180,000 per year, depending on experience and qualifications
• 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
• Collaboration with experienced ML scientists, engineers, and clinical professionals
• Opportunities to contribute to publications and conference presentations
• Inclusive and equal-opportunity workplace