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Freelance Machine Learning Biology Jobs in Oregon

Natera

Machine Learning Scientist, Multimodal AI

OR · On-site +1

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 ...

Nextdoor

Senior/Staff Machine Learning Engineer - Ads

OR · On-site +1

$205K - $355K/yr

S. in Computer Science, Applied Math, Statistics, Computational Biology, or a related field * 5+ years of industry/academic experience in applying machine learning at scale * Experience in building ...

LSU AgCenter

Assistant/Associate Professor (Computational Biology/Data Science) Position Type:Faculty Department ... machine learning, artificial intelligence, remote sensing, Geographic Information Systems (GIS ...

Louisiana State University

Assistant/Associate Professor (Computational Biology/Data Science) Position Type:Faculty Department ... machine learning, artificial intelligence, remote sensing, Geographic Information Systems (GIS ...

Louisiana Workforce Commission

Assistant/Associate Professor (Computational Biology/Data Science) Position Type:Faculty Department ... machine learning, artificial intelligence, remote sensing, Geographic Information Systems (GIS ...

Natera

Staff Scientist, Bioinformatics/RWD

OR · On-site +1

This unique role blends expertise in bioinformatics, artificial intelligence (AI), machine learning ... D. in Bioinformatics, Computational Biology, Genetics, or a related field * At least 10 years of ...

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Freelance Machine Learning Biology information

What is the difference between Freelance Machine Learning Biology vs Freelance Data Scientist?

AspectFreelance Machine Learning BiologyFreelance Data Scientist
Required CredentialsBackground in biology, machine learning, and data analysisBackground in statistics, programming, and data analysis
Work EnvironmentResearch labs, biotech firms, or remote projects focused on biological dataVarious industries including finance, healthcare, and tech, often remote
Employer & Industry UsageBiotech companies, research institutions, healthcare startups

Freelance Machine Learning Biology specializes in applying machine learning techniques to biological data, often within biotech and healthcare sectors. Freelance Data Scientists have a broader scope across industries, focusing on data analysis and modeling in various fields. While both roles require strong analytical skills, Freelance Machine Learning Biology emphasizes biological knowledge combined with machine learning expertise.

What are the most commonly searched types of Machine Learning Biology jobs in Oregon?

The most popular types of Machine Learning Biology jobs in Oregon are:

What are popular job titles related to Freelance Machine Learning Biology jobs in Oregon?

For Freelance Machine Learning Biology jobs in Oregon, the most frequently searched job titles are:

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The top searched job categories for Freelance Machine Learning Biology jobs in Oregon are:

What cities in Oregon are hiring for Freelance Machine Learning Biology jobs?

Cities in Oregon with the most Freelance Machine Learning Biology job openings:

Freelance Machine Learning Biology jobs near you

Machine Learning Scientist, Multimodal AI

Natera

OR • On-site, Remote

Full-time

Re-posted 22 days ago

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 38 frontline employees who took The Breakroom Quiz

56th of 120 rated laboratories

Job description

POSITION SUMMARY:

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. You will collaborate with scientists, pathologists, bioinformaticians, and software engineers to scale machine learning approaches that advance personalized oncology diagnostics and tumor-informed minimal residual disease (MRD) testing.

PRIMARY 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

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

PREFERRED QUALIFICATIONS:

  • 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)

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