The posting
About Us: Artera is an artificial intelligence company dedicated to transforming cancer care. We’ve developed foundation models that analyze clinical and pathology data, generating actionable insights that guide therapy selection and improve outcomes for cancer patients. By continuously improving these models, we aim to uncover the biological mechanisms driving cancer progression.
We're looking for an experienced machine learning engineer and technical lead 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. You'll provide technical direction for complex, cross-functional programs, mentor other ML scientists and engineers, and drive decisions across the model-development lifecycle. 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:
- Serve as the technical lead for patient-facing AI products, defining the technical strategy and driving execution 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.
- Lead technical planning and delivery: break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risks 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.
- Provide technical leadership and mentorship to 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 serving as a technical lead for complex machine-learning programs, including making technical decisions, coordinating work across engineers / scientists and cross-functional partners, and launching and monitoring ML 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 ability to lead complex technical programs — scoping problems, technical direction, sequencing work, 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.
Equal Employee Opportunity:At Artera, we value bringing together individuals from diverse backgrounds to develop new andinnovative solutions for patients and physicians. As an equal opportunity employer, we do notdiscriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy),physical or mental disability, medical condition, genetic information gender identity orexpression, sexual orientation, marital status, protected veteran status, or any other legallyprotected characteristic.
