artera - Senior/Staff Machine Learning Engineer (Tech Lead, Model Dev)
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. • 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.
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.
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