Lead Machine Learning and Bioinformatics Scientist

LOCATION: Position is available as a hybrid position in San Carlos, Bay Area, California as well as a remote position within the US.

Natera is currently seeking a highly skilled and innovative Lead Machine Learning Scientist to contribute to cutting-edge research and development within our Bioinformatics Research and Development team, focused on Epigenomics in Oncology. Natera’s mission is to change the management of disease worldwide by using information gained from a simple blood draw to detect disease early and proactively inform treatment. 

The ideal candidate will have a strong background in machine learning and statistical pattern recognition, as well as genomics and sequencing data analysis, with experience applying both classical and modern machine learning methods to large-scale biological data. This individual will play a critical role in developing, validating, and advancing state-of-the-art machine learning approaches for cancer detection and monitoring.

PRIMARY RESPONSIBILITIES:

• Design, implement, and validate cutting-edge machine learning and statistical methods to solve problems in cancer diagnostics

• Contribute to best practices in model interpretability, uncertainty estimation, and reproducibility

• Design robust feature engineering and extraction pipelines tailored to biological data 

• Prototype and productionize models using scalable ML infrastructure tools such as MLflow, Airflow, and Docker

• Collaborate closely with molecular biologists on experimental design, ensuring data integrity and quality control

• Foster a culture of innovation, collaboration, and scientific excellence

• Actively participate in code and design reviews

QUALIFICATIONS:

• PhD in Computer Science, Machine Learning, Statistics, Bioinformatics, or a related quantitative field with a strong emphasis on cancer epi/genomics and 6+ years of experience post-PhD.

• Deep expertise in the theory and practical development of core machine learning models, including generalized linear models, kernel methods, tree-based algorithms, and neural networks, with a focus on biological data (e.g., DNA/RNA sequencing data)

• Proficiency in Python and its scientific computing stack (e.g., NumPy, Pandas, Scikit-learn)

• Strong cross-functional communication skills, with the ability to collaborate effectively across disciplines

• High scientific rigor and a growth mindset, with enthusiasm for both teaching and learning new computational and biological concepts

• Nice to have: Hands-on experience with state-of-the-art deep learning models, large language models (LLMs), and multimodal foundation models

LOCATION: Position is available as a hybrid position in San Carlos, Bay Area, California as well as a remote position within the US.

Natera is currently seeking a highly skilled and innovative Lead Machine Learning Scientist to contribute to cutting-edge research and development within our Bioinformatics Research and Development team, focused on Epigenomics in Oncology. Natera’s mission is to change the management of disease worldwide by using information gained from a simple blood draw to detect disease early and proactively inform treatment. 

The ideal candidate will have a strong background in machine learning and statistical pattern recognition, as well as genomics and sequencing data analysis, with experience applying both classical and modern machine learning methods to large-scale biological data. This individual will play a critical role in developing, validating, and advancing state-of-the-art machine learning approaches for cancer detection and monitoring.

PRIMARY RESPONSIBILITIES:

• Design, implement, and validate cutting-edge machine learning and statistical methods to solve problems in cancer diagnostics

• Contribute to best practices in model interpretability, uncertainty estimation, and reproducibility

• Design robust feature engineering and extraction pipelines tailored to biological data 

• Prototype and productionize models using scalable ML infrastructure tools such as MLflow, Airflow, and Docker

• Collaborate closely with molecular biologists on experimental design, ensuring data integrity and quality control

• Foster a culture of innovation, collaboration, and scientific excellence

• Actively participate in code and design reviews

QUALIFICATIONS:

• PhD in Computer Science, Machine Learning, Statistics, Bioinformatics, or a related quantitative field with a strong emphasis on cancer epi/genomics and 6+ years of experience post-PhD.

• Deep expertise in the theory and practical development of core machine learning models, including generalized linear models, kernel methods, tree-based algorithms, and neural networks, with a focus on biological data (e.g., DNA/RNA sequencing data)

• Proficiency in Python and its scientific computing stack (e.g., NumPy, Pandas, Scikit-learn)

• Strong cross-functional communication skills, with the ability to collaborate effectively across disciplines

• High scientific rigor and a growth mindset, with enthusiasm for both teaching and learning new computational and biological concepts

• Nice to have: Hands-on experience with state-of-the-art deep learning models, large language models (LLMs), and multimodal foundation models