Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients.

PRIMARY RESPONSIBILITIES:

• Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data

• Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group

• Prototype, test and productionize ML models using scalable infrastructure

• Own key technical deliverables within larger projects and help shape the team’s research roadmap

• Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies

• Keep raising the bar through code and design reviews across projects

• Translate complex results into clear stories for technical and non-technical audiences

• Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence

QUALIFICATIONS:

• PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD

• Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data

• Proven track record of innovation and technical leadership in complex method development projects

• Expertise in deep learning and multimodal foundation models

• Proficient in Python and its scientific computing ecosystem

• Scientific rigor throughout the R&D process, with eagerness to teach and learn new computational methods and biology

• Excellent communication skills and a collaborative approach across teams

Preferred Experience:

• Familiarity with DNA methylation and other epigenomic data

• Experience with cell-free DNA and liquid biopsy samples

• Background in cancer genomics and diagnostics

• Hands-on work with multimodal foundation models applied to biological data