Note: The job is a remote job and is open to candidates in USA. ArteraAI develops AI tools for cancer care. The Senior/Staff Machine Learning Engineer will lead AI biomarker development from clinical problem framing through validation, regulatory submission, and production deployment, while guiding cross-functional programs and advancing pathology foundation models.
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
Skills
- 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
- 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
Benefits
- Remote work (United States)
Company Overview
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