Senior/Staff Machine Learning Engineer (Model Dev)

We're looking for an experienced machine learning engineer to own AI biomarker development end to end — from problem framing with clinical and biostatistics partners, through model development and validation, to regulatory submission and production deployment. Beyond owning a biomarker program, you'll take on the hardest cross-cutting problems in our field: robustness across scanners and sites, mechanistic interpretability of model decisions, and the next generation of our pathology foundation models.

Essential Responsibilities:

• 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.

Experience Requirements:

• 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.

Desired:

• 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.