Senior/Staff Machine Learning Engineer – Model Dev

This is a fully remote position, open to applicants in United States.

📋 Description

• Direct the technical initiatives and establish the strategic vision for patient-centered products in collaboration with product, biostatistics, clinical development, and regulatory/quality teams.

• Create and develop AI-driven biomarkers utilizing whole-slide images, clinical variables, and molecular data to forecast patient outcomes, treatment advantages, and molecular characteristics.

• Enhance self-supervised foundational models and subsequent architectures, including multiple-instance learning, time-to-event/hazard models, segmentation, and classification.

• Ensure score reproducibility across various scanners, institutions, staining protocols, and patient demographics.

• Design and incorporate mechanistic interpretability techniques to elucidate model decisions and enhance models.

• Develop tools and processes for the comprehensive model development lifecycle, from prototyping to production deployment and monitoring.

• Prepare and defend regulatory and quality documentation while representing AI in design and development evaluations.

• Organize and oversee multi-quarter delivery milestones, dependencies, risks, submission timelines, and launch dates.

• Publish findings in peer-reviewed journals and present at clinical and machine learning forums; facilitate external partnerships.

• Mentor and guide machine-learning scientists and engineers, elevating standards for scientific rigor, code quality, and effective communication.

⛳️ Requirements

• Over 5 years of industry experience in developing deep learning systems using PyTorch (or TensorFlow).

• At least 2 years of experience as a technical lead, overseeing the launch and monitoring of machine-learning products in production settings.

• Proven expertise in oncology and biomarker development, including knowledge of cancer biology, treatment pathways, clinical endpoints, risk stratification, and clinically actionable biomarkers.

• Demonstrated project management skills, including scoping, sequencing, and managing dependencies and risks across multiple teams with strict timelines.

• Established ability to convey complex machine learning concepts clearly to cross-functional, non-ML partners.

• Experience in mentoring or managing machine learning scientists and engineers.

• Proficient in building ML applications on intricate clinical data, such as medical imaging, multi-omics, longitudinal patient records, weakly supervised learning, and variability across sites, devices, and protocols.

• Background in developing ML in regulated environments, such as FDA 510(k)/De Novo, CE/UKCA, SaMD, design controls, or CLIA/LDT validation.

• Familiarity with self-supervised representation learning and adapting medical foundation models for downstream clinical applications.

• Experience with randomized controlled trial data and multi-institutional clinical cohorts.

• Record of peer-reviewed publications and conference presentations, with collaborations in academic or industry settings.

• Experience with cloud-scale training, workflow orchestration, experiment tracking, and reproducible ML pipelines.

🏝️ Benefits

• Equity

• 401k matching

• Unlimited paid time off (PTO)