Staff Machine Learning Scientist, Translational AI

Position Summary

We are seeking a Staff Machine Learning Scientist – Translational AI to provide technical leadership at the intersection of deep learning foundation models, computational biology, and molecular diagnostics. This ownership role drives the architecture and validation of genomic, transcriptomic, and multimodal sequence models to accelerate patient stratification, target identification, and therapeutic monitoring across our cell-free DNA (cfDNA) and multi-omic platforms. This Staff-level position operates with broad technical autonomy, driving modeling strategy across multiple concurrent portfolios while maintaining direct execution responsibilities in model compilation, scaling, and testing. Working within a builder framework, you will align across AI Research, Bioinformatics, and Clinical Science divisions to transition advanced representation learning models into reproducible, clinically valid diagnostic assets.

Primary Responsibilities

Scientific Leadership in Translational AI

• Serve as the principal technical authority on the deployment of molecular, genomic, and pathology foundation models applied to oncology and translational medicine questions
• Engineer rigorous alignment and post-training workflows that ground pre-trained foundation models in empirical clinical trial and molecular diagnostic data, eliminating speculative modeling assumptions
• Formulate objective peer-review frameworks and deliver technical feedback to elevate the modeling code, experimental standards, and scientific designs of the broader AI research group

Foundation Models to Biological and Clinical Translation

• Lead the post-training, parameter-efficient fine-tuning (PEFT), and evaluation of deep sequence, multimodal, and representation learning models for biomarker discovery, molecular recurrence monitoring, and therapeutic response forecasting
• Design robust fine-tuning, probing, and latent space representation analysis workflows that extract interpretable, biologically grounded patterns from high-dimensional transformer architectures
• Validate model outputs against multi-omic benchmarks and real-world outcomes, ensuring model predictions deliver the exact deterministic accuracy required for patient tracking and clinical interventions

Modeling, Experimentation, and Evaluation

• Build, train, and optimize advanced machine learning models utilizing next-generation sequencing (NGS), ctDNA assays, digital pathology imaging, and longitudinal clinical metadata
• Design rigorous clinical investigation and evaluation frameworks that connect model performance metrics (e.g., loss curves, precision-recall) directly to translational utility and real-world distribution shifts
• Systematically identify algorithmic failure modes, sources of dataset bias, and covariate shift, implementing robust mitigation strategies suitable for regulated, clinical-facing pipelines

Cross-Functional Collaboration and Influence

• Partner with Computational Biology, Translational Science, and Medical Affairs teams to translate complex clinical requirements into clear, quantitative machine learning problem statements
• Act as a systems-level technical bridge between AI Research and ML Engineering teams to ensure that validation models convert seamlessly into scalable, reproducible production workflows
• Provide technical leadership and data execution support for strategic external collaborations, pharmaceutical partnerships, and foundation model research consortiums

Scientific Communication and External Presence

• Translate complex multimodal model architectures and performance metrics into transparent, high-integrity data packages for clinical and scientific stakeholders