Machine Learning Scientist, Multimodal AI

Note

The job is a remote job and is open to candidates in USA. Natera is a global leader in cell-free DNA testing for oncology, women’s health, and organ health. The Machine Learning Scientist will develop and deploy multimodal deep learning systems across imaging, genomic, transcriptomic, and clinical data to advance personalized oncology diagnostics and tumor-informed minimal residual disease testing. The role also involves building production-quality workflows, collaborating with technical and clinical teams, and translating research prototypes into validated tools.

Responsibilities

• 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

Skills

• 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
• 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)

Benefits

• Comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents.
• Natera employees and their immediate families receive free testing.
• Fertility care benefits.
• Pregnancy and baby bonding leave.
• 401k benefits.
• Commuter benefits.
• Generous employee referral program.

Company Overview

• Natera specializes in cell-free DNA testing to provide a more targeted interventions to oncology, women's health, and organ health. It was founded in 2004, and is headquartered in San Carlos, California, USA, with a workforce of 5001-10000 employees. Its website is https://www.natera.com/.

Company H1B Sponsorship

• Natera has a track record of offering H1B sponsorships, with 13 in 2026, 90 in 2025, 72 in 2024, 37 in 2023, 71 in 2022, 64 in 2021, 40 in 2020. Please note that this does not guarantee sponsorship for this specific role.