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Entry Level Machine Learning Engineer Jobs in Oregon

Natera

OR · On-site

You will collaborate with scientists, pathologists, bioinformaticians, and software engineers to scale machine learning approaches that advance personalized oncology diagnostics and tumor-informed ...

Numerator

... Machine Learning team ... This is a 50/50 player-coach role: you'll directly manage a small team of ML engineers while ...

Numerator

... Machine Learning team ... This is a 50/50 player-coach role: you'll directly manage a small team of ML engineers while ...

Nike

Senior Software Engineer

Beaverton, OR · On-site

$127K - $168K/yr

Serve as an integral member of a multi-functional engineering teams that delivers solutions unlocking machine learning for Nike; analyze and profile data to uncover insights in support of scalable ...

Upstart

Software Engineer, Distribution Platform

OR · On-site +1

As a Software Engineer on the Distribution Platform team at Upstart, you will be instrumental in ... You will work closely with cross-functional counterparts in Analytics, Marketing, Machine Learning ...

Pwc

US Tech - AI Engineering Senior Associate

Portland, OR · On-site

$55K - $187K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

Smarsh

OR · On-site

On any given day, you will have the opportunity to interface with business leaders, machine learning researchers, data engineers, platform engineers, data scientists and many more, enabling you to ...

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All JobsMachine Learning Engineer JobsEntry Level Machine Learning Engineer JobsEntry Level Machine Learning Engineer Jobs in Oregon

Entry Level Machine Learning Engineer information

See Oregon salary details

$31.7K

$73.3K

$124.8K

How much do entry level machine learning engineer jobs pay per year?

As of Jul 10, 2026, the average yearly pay for entry level machine learning engineer in Oregon is $73,335.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,400.00 and $83,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are some typical projects or tasks an Entry Level Machine Learning Engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What is an Entry Level Machine Learning Engineer job?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are the most commonly searched types of Machine Learning Engineer jobs in Oregon? The most popular types of Machine Learning Engineer jobs in Oregon are:
What are popular job titles related to Entry Level Machine Learning Engineer jobs in Oregon? For Entry Level Machine Learning Engineer jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Entry Level Machine Learning Engineer jobs in Oregon look for? The top searched job categories for Entry Level Machine Learning Engineer jobs in Oregon are:
What cities in Oregon are hiring for Entry Level Machine Learning Engineer jobs? Cities in Oregon with the most Entry Level Machine Learning Engineer job openings:
Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Oregon as of July 2026, with employment types broken down into 1% Locum Tenens, 92% Full Time, 4% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $73,335 per year, or $35.3 per hour.
Machine Learning Scientist, Multimodal AI

Machine Learning Scientist, Multimodal AI

Natera

OR • On-site

Other

Re-posted 11 days ago

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

50th of 103 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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