Machine Learning Scientist, Multimodal AI
$124,800–$171,600 year
Remote · United States
Job Summary
Design, implement, and evaluate deep learning models across biomedical modalities (histopathology imaging, genomics, transcriptomics, and cfDNA); develop multimodal AI architectures integrating imaging with molecular and clinical data; build scalable, production-ready ML workflows on AWS; apply CNNs, transformers, and representation learning; collaborate with cross-functional teams to translate prototypes into validated tools; analyze model outputs to generate reproducible biological and clinical insights; document pipelines and communicate findings to stakeholders.
Required Qualifications
- 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
Desired Qualifications
- 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
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