Staff ML & Bioinformatics Scientist—Cancer Detection

Natera; San Carlos (CA); On-site; USD 170,000 - 212,000; Full time

Job summary

Natera is seeking a Staff Machine Learning and Bioinformatics Scientist to design models for Multi-Cancer Early Detection. You will work with large-scale genomic data, collaborating with biologists and clinicians to translate findings into clinically validated tools.

Responsibilities include developing novel ML/statistical methods, building interpretable models, prototyping at scale, and mentoring junior scientists to sustain scientific excellence.

Qualifications

• PhD required in a quantitative field with 6–12 years post-PhD experience.
• Hands-on experience with statistical and ML methods on complex biological data.
• Strong leadership and ability to guide research from prototype to validation.

Responsibilities

• Lead development of novel statistical and ML methods for cancer signal detection on multi-modal genomic data.
• Build interpretable, well-calibrated models with uncertainty estimation and reproducibility.
• Prototype, test and productionize ML models using scalable infrastructure.
• Own technical deliverables and shape the research roadmap.
• Collaborate with molecular biologists and clinical scientists on experiments and validation.
• Share code and design improvements across projects.
• Translate results into clear stories for technical and non-technical audiences.
• Mentor junior scientists and foster collaboration and scientific excellence.

Skills

Machine Learning

Bioinformatics

Deep Learning

Python

Statistical Methods

Genomics

Multi-modal Data

Education

PhD in Computer Science / Mathematics / Bioinformatics

Tools

Python

Job description

Natera is seeking a Staff Machine Learning and Bioinformatics Scientist to design models for Multi-Cancer Early Detection. You will work with large-scale genomic data, collaborating with biologists and clinicians to translate findings into clinically validated tools.

Responsibilities include developing novel ML/statistical methods, building interpretable models, prototyping at scale, and mentoring junior scientists to sustain scientific excellence.