Staff Machine Learning Scientist - Translational AI

Staff Machine Learning Scientist - Translational AI

Position Summary

Natera is seeking a Staff Machine Learning Scientist – Translational AI to provide technical leadership at the intersection of biomedical foundation models, computational biology, and clinical translation. This role is responsible for shaping how genomic, pathology and multimodal foundation models are applied to high-impact translational problems, including target identification, drug and biomarker discovery, patient stratification, and therapeutic development.

As a Staff-level scientist, you will operate with broad technical autonomy, influencing modeling strategy across multiple initiatives while remaining hands-on in model development, experimentation, and interpretation. You will work closely with AI scientists, translational scientists, bioinformatics, clinical partners, and ML engineers to ensure foundation models deliver biologically grounded and clinically meaningful insights.

 

 

Key Responsibilities

Scientific Leadership in Translational AI

• Serve as a scientific and technical authority on the application of molecular, genomic and pathology foundation models to translational and clinical questions.
  
  

• Define modeling strategies that bridge pretrained foundation models and downstream translational use cases.
  
  

• Review and elevate modeling approaches used by other scientists through technical feedback and mentorship.
  
  

Foundation Models to Biological & Clinical Translation

• Lead the application and post-training of foundation models (deep sequence, multimodal, representation learning) for biomarker discovery, outcome prediction, molecular recurrence modeling, and therapy response assessment.
  
  

• Design fine-tuning, probing, and representation analysis workflows that extract biologically interpretable signals from large models.
  
  

• Ensure modeling outputs are aligned with biological plausibility, clinical relevance, and downstream decision-making needs.
  
  

Modeling, Experimentation