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Machine Learning Biomedical Engineer Jobs (NOW HIRING)

Natera

OR · On-site

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 ...

Pilgrim

Biomedical Engineer

Redwood City, CA · On-site

As a Biomedical Engineer at Pilgrim, you will be a hands-on member of our engineering team, driving ... Prototype parts using 3D printing (FDM/SLA), benchtop machining, laser cutting, bonding, and other ...

CoVar

Machine Learning Engineer

Durham, NC

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... We develop AI/ML tools to help the DoD detect enemies and threats, help biomedical researchers find ...

Mass General Brigham

Biomedical Engineer Senior

Boston, MA · On-site

$93.95K - $136.74K/yr

... biomedical engineers. -Leads large-scale projects across an entity. -Approves new equipment by ... machine-patient interaction, conferring with equipment users, developing modifications, and ...

Massgeneralbrigham

Biomedical Engineer Senior

Boston, MA · On-site

$93.95K - $136.74K/yr

... biomedical engineers. -Leads large-scale projects across an entity. -Approves new equipment by ... machine-patient interaction, conferring with equipment users, developing modifications, and ...

Natera

Machine Learning Scientist, Multimodal AI

$124.80K - $156K/yr

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 ...

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$110.80K - $152.20K/yr

The Sr Engineer, AI & ML will be responsible for developing AI and ML models, working with data ... for Biomedical Signal Processing systems • Design, train and evaluate machine learning models ...

Apple

Machine Learning Engineer

San Diego, CA

$139.50K - $258.10K/yr

... Biomedical Engineering, Statistics, Applied Mathematics, or related field, or equivalent industry experience. Strong foundation in machine learning, statistics, signal processing, or applied ...

Apple

Machine Learning Engineer

Cupertino, CA

$147.40K - $272.10K/yr

... Biomedical Engineering, Statistics, Applied Mathematics, or related field, or equivalent industry experience. Strong foundation in machine learning, statistics, signal processing, or applied ...

Neuralink

Neuroengineer Intern

South San Francisco, CA · On-site

$35/hr

Evidence of exceptional ability in neuroscience, machine learning, biomedical engineering, or a related field * 2+ years of academic or industry experience * A strong understanding of the scientific ...

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All JobsMachine Learning Biomedical Engineer Jobs

Machine Learning Biomedical Engineer information

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$31.5K

$128.8K

$193.5K

How much do machine learning biomedical engineer jobs pay per year?

As of Jun 4, 2026, the average yearly pay for machine learning biomedical engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Biomedical Engineer, and why are they important?

To thrive as a Machine Learning Biomedical Engineer, you need a strong background in biomedical engineering, data analysis, and machine learning, typically supported by a degree in biomedical engineering, computer science, or a related field. Familiarity with programming languages like Python or R, machine learning frameworks (e.g., TensorFlow, PyTorch), and experience with medical imaging or signal processing tools are commonly required. Critical thinking, problem-solving, and the ability to communicate complex technical concepts to interdisciplinary teams are vital soft skills. These abilities are crucial for developing innovative healthcare solutions, ensuring regulatory compliance, and bridging the gap between technology and medicine.

How does a Machine Learning Biomedical Engineer typically collaborate with clinicians and researchers in a healthcare setting?

Machine Learning Biomedical Engineers often work closely with clinicians and researchers to develop algorithms that solve real-world medical challenges. Collaboration usually involves understanding clinical needs, translating them into technical requirements, and iteratively refining models based on feedback from medical experts. Regular meetings, interdisciplinary project teams, and direct participation in data collection or validation studies are common. This collaborative environment ensures that technical solutions are both innovative and clinically relevant, making communication and adaptability essential skills.

What does a Machine Learning Biomedical Engineer do?

A Machine Learning Biomedical Engineer applies machine learning techniques to solve problems in biology and medicine. They develop algorithms and models to analyze complex biomedical data, such as medical images, genetic information, or sensor readings. Their work supports advancements in diagnostics, treatment planning, and personalized medicine. Typically, they collaborate with clinicians, researchers, and other engineers to design systems that improve healthcare outcomes.

What is the difference between Machine Learning Biomedical Engineer vs Data Scientist in Biomedical Industry?

AspectMachine Learning Biomedical EngineerData Scientist in Biomedical Industry
Required CredentialsDegree in Biomedical Engineering, Computer Science, or related fields; knowledge of machine learning and biomedical dataDegree in Data Science, Statistics, or related fields; proficiency in data analysis and machine learning
Work EnvironmentResearch labs, healthcare institutions, biotech companiesHealthcare analytics firms, research institutions, biotech companies
Employer & Industry UsageDevelops algorithms for medical devices, diagnostics, and treatment planningAnalyzes biomedical data to inform clinical decisions, research, and product development

Both roles require expertise in machine learning and biomedical data, but Machine Learning Biomedical Engineers focus on developing algorithms for medical applications, while Data Scientists analyze biomedical data to support research and clinical decisions.

More about Machine Learning Biomedical Engineer jobs
What cities are hiring for Machine Learning Biomedical Engineer jobs? Cities with the most Machine Learning Biomedical Engineer job openings:
What states have the most Machine Learning Biomedical Engineer jobs? States with the most job openings for Machine Learning Biomedical Engineer jobs include:
What job categories do people searching Machine Learning Biomedical Engineer jobs look for? The top searched job categories for Machine Learning Biomedical Engineer jobs are:
Machine Learning Biomedical Engineer jobs near you
Infographic showing various Machine Learning Biomedical Engineer job openings in the United States as of May 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Temporary. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Scientist, Multimodal AI

Natera

On-site

Other

Posted 5 days ago

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

47th 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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