Natera is hiring a Machine Learning Scientist to join our AI and computational biology team. This role develops and deploys deep learning models across digital pathology, genomics, transcriptomics, and cell-free DNA (cfDNA) modalities. You will build multimodal AI systems that integrate imaging, molecular, and clinical data, leveraging proprietary genomic and clinical datasets.

Company: Natera

Role: Machine Learning Scientist, Multimodal AI

Location: US Remote

Experience:

PhD in Computer Science, Computational Biology, Biomedical Engineering, Bioinformatics, Statistics, or a related quantitative discipline with a focus on machine learning or AI
Core experience developing machine learning models for biomedical applications, specifically in medical imaging, computational pathology, genomics, transcriptomics, multi-omics, or molecular diagnostics
Hands-on expertise with PyTorch and strong production-level programming skills in Python
Practical application of deep learning architectures such as CNNs, transformers, attention mechanisms, and representation learning
Experience managing datasets and training workflows within distributed or cloud computing environments (AWS)
Proven ability to take ownership of research projects and translate prototypes into robust, deployment-ready workflows
Experience adapting pre-trained foundation models for downstream biomedical applications

Key Skills:

PyTorch
Python
deep learning models
CNNs
transformers
vision transformers (ViTs)
sequence transformers
representation learning
foundation model fine-tuning
cloud infrastructure (AWS)

Qualification:

PhD in Computer Science, Computational Biology, Biomedical Engineering, Bioinformatics, Statistics, or a related quantitative discipline with a focus on machine learning or AI

Role Focus:

Design, implement, and evaluate deep learning models across biomedical data modalities, including histopathology imaging, genomic sequencing, transcriptomics, and cfDNA features
Develop multimodal AI architectures that integrate H&E whole-slide imaging data with molecular and clinical data sources
Build scalable, production-quality machine learning workflows and pipelines using cloud infrastructure (AWS)
Apply modern machine learning techniques including convolutional neural networks (CNNs), vision transformers (ViTs), sequence transformers, representation learning, and foundation model fine-tuning
Collaborate across technical and clinical teams to translate machine learning prototypes into validated tools
Analyze model outputs to generate reproducible biological and clinical insights
Document pipelines thoroughly and communicate data-driven findings clearly to cross-functional stakeholders

Nice to have:

Experience integrating imaging, molecular, and clinical data within unified multimodal machine learning frameworks
Technical familiarity with DNA sequencing, RNA sequencing, methylation, and ctDNA assays
Hands-on experience with digital pathology software and whole-slide imaging analysis
Exposure to survival modeling, longitudinal prediction, or time-to-event modeling
Experience applying self-supervised learning, weakly supervised learning, or multiple instance learning (MIL) to clinical data
Domain knowledge in oncology, biomarker discovery, or clinical precision medicine
Track record of peer-reviewed publications in machine learning or computational biology conferences and journals (e.g., NeurIPS, ICML, CVPR, MICCAI, Nature Biomedical Engineering)

Other:

Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents
Free testing for employees and their immediate families
Fertility care benefits
Pregnancy and baby bonding leave
401k benefits
Commuter benefits
Generous employee referral program
Remote USA
$124,800 - $171,600 USD

Machine Learning Scientist, Multimodal AI

Skills Required

Description