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.

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.