Lead the technical effort and define the strategic vision for patient-facing products, in partnership with product, biostatistics, clinical development, and regulatory/quality.

Design and build AI-based biomarkers on multimodal data — including whole-slide images, clinical variables, and molecular data — to predict patient outcomes, treatment benefit, and molecular traits.

Advance our core self-supervised foundation models and the downstream architectures built on them (multiple-instance learning, time-to-event / hazard models, segmentation and classification components), with generalization as a first-order objective.

Own score reproducibility across scanners, institutions, staining protocols, and patient populations.

Develop and integrate mechanistic interpretability methods to explain model decisions, build clinician trust, and drive actionable model improvements.

Architect tools and processes that streamline the end-to-end model development lifecycle — from prototyping through production deployment and monitoring — ensuring efficiency, reproducibility, regulatory compliance, and scale.

Author and defend regulatory and quality documentation, and represent AI in design and development reviews.

Plan and manage delivery: break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risk early, and hold submission and launch dates.

Publish in peer-reviewed journals and present at clinical and ML venues; support external academic and industry collaborations.

Mentor and coach machine-learning scientists and engineers, fostering their technical growth and collaboration skills, and raise the bar on scientific rigor, code quality, and written communication across the team.

5+ years of industry experience building deep learning systems in PyTorch (or TensorFlow).

2+ years of experience as a technical lead, launching and monitoring machine-learning products in production environments.

Demonstrated depth in oncology and biomarker development: familiarity with cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable.

Demonstrated project management ability — scoping, sequencing, and managing dependencies and risk across multiple teams on dated deliverables.

Proven ability to communicate complex ML concepts effectively to cross-functional, non-ML collaborators.

Experience mentoring or managing ML scientists and engineers.

Experience building ML on complex clinical data — medical imaging, multi-omics, or longitudinal patient records — including weakly supervised learning and handling variation across sites, devices, and protocols.

Experience developing ML in a regulated environment — FDA 510(k)/De Novo, CE/UKCA, SaMD, design controls, or CLIA/LDT validation.

Experience with self-supervised representation learning (e.g., DINOv3) and adapting medical foundation models to downstream clinical tasks.

Experience with data from randomized controlled trials and multi-institutional clinical cohorts.

Peer-reviewed publications and conference presentations; history of external academic or industry collaborations.

Experience with cloud-scale training and workflow orchestration (e.g., Flyte / Union, Kubernetes, AWS), experiment tracking, and reproducible ML pipelines.