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Freelance Deep Learning Engineer Jobs in Oregon (NOW HIRING)

Natera

OR

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 ...

Nvidia

OR

You will work with the latest accelerated computing and Deep Learning software and hardware platforms, and with many scientific researchers, developers, and customers to craft improved workflows and ...

Nvidia

OR

$122K - $161K/yr

We are hiring software engineers for the CUDA Tile team ... NVIDIA GPUs are at the center of the deep learning revolution and continue to enable breakthroughs ...

Vectara

OR

$122K - $161K/yr

We are the developers of the Hughes Hallucination Evaluation Model and Correction model, core to ... Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval ...

Nvidia

OR

$122K - $161K/yr

We are hiring software engineers for the CUDA Tile team ... NVIDIA GPUs are at the center of the deep learning revolution and continue to enable breakthroughs ...

Cresta

Senior Machine Learning Engineer

OR · On-site +1

$205K - $270K/yr

Machine Learning Engineers at Cresta work across several high-impact AI initiatives. Final team ... This track requires strong pre-LLM ML foundations, deep expertise in LLMs and modern prompting ...

Netflix

OR

$466K - $750K/yr

We are looking for an experienced Machine Learning Engineer with deep expertise in training and inference efficiency for Large Language Models (LLMs), Multimodal LLMs, and other media ML models. In ...

Nvidia

OR · On-site

Be part of a team that is at the center of deep-learning compiler technology spanning architecture ... Strong background in software engineering principles with a focus on crafting robust and ...

New

Cotiviti

$125K - $172K/yr

Overview We are looking for a Senior Principal Machine Learning Engineer to lead the design and ... Deep expertise in two or more of: LLM evaluation, retrieval-augmented generation (RAG), ranking, or ...

Cresta

OR · On-site

$104K - $143K/yr

About the role We are looking for a Senior Machine Learning Engineer, Voice Experience to help ... Deep experience with model evaluation, benchmarking, error analysis, and quality improvement for ...

Zillow

OR · On-site

$194K - $310K/yr

Design and develop multiple agent interaction modes, including Voice AI and Deep Research ... Who you are As a Principal Machine Learning Engineer, you are a roll-up-the-sleeves and get-it-done ...

Varsity Tutors

Machine Learning Tutor

Eugene, OR · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Varsity Tutors

Machine Learning Tutor

OR · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Varsity Tutors

Machine Learning Tutor

Portland, OR · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

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Freelance Deep Learning Engineer jobs near you
Machine Learning Scientist, Multimodal AI

Machine Learning Scientist, Multimodal AI

Natera

OR

Other

Posted 17 days ago

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

51st of 105 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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Hours and flexibility

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