Staff Machine Learning Scientist, Translational AI

Role: Lead scientific authority in Translational AI for genomic and pathology foundation models, focusing on oncology applications. Responsibilities: drive architecture, validation, and application of deep learning models for biomarker discovery, patient stratification, and therapeutic monitoring across cfDNA and multi-omic platforms; collaborate with AI Research, Bioinformatics, and Clinical Science teams; translate complex models into reproducible, clinically valid diagnostic assets; contribute to external scientific dissemination. Requirements: PhD in a quantitative field (e.g., Computer Science, Bioinformatics); 5+ years industry/postdoctoral experience applying deep learning to genomic/clinical datasets, with emphasis on oncology; expertise in transformer architectures, representation learning, and ML infrastructure (PyTorch, HuggingFace, MLflow); proven track record of translating ML outputs into biological/clinical insights. Preferred: experience with multimodal models combining genomic, imaging, and EHR data; handling clinical trial or real-world evidence datasets; strong publication record. High-Value: oncology, cell-free DNA, multi-omic platforms, clinical validation, regulatory considerations, cloud computing. Collaboration: cross-functional with AI, Bioinformatics, Clinical, and external partners. Work setup: remote USA; salary range $163,200–$220,000.