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Machine Learning Biomedical Engineer Jobs in Berkeley, CA

Machine Learning Engineer

San Francisco, CA ยท On-site +1

$187K - $260K/yr

Collaborate with other engineers to improve the recommendation systems and models that power personalization and discovery. Train, evaluate, and deploy sophisticated machine learning models to ...

Machine Learning Engineer

San Francisco, CA ยท On-site

$100K - $150K/yr

The Opportunity As a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the ...

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

This job We're looking for a machine learning engineers who can work on large-scale image and video models training experiments.. Some stuff you can do: * Train foundation diffusion models for image ...

๐Ÿš€ Machine Learning Engineer / Member of Technical Staff, ML & Optimization ๐Ÿ“ San Francisco ๐Ÿ’ฐ $160K - $250K + equity ๐Ÿข Confidential AI Startup We're partnering with an early-stage AI ...

They are seeking a Machine Learning Engineer to train and deploy critical models for their core product, focusing on interpreting unstructured data and improving model performance. Responsibilities ...

Machine Learning Engineer

San Francisco, CA ยท On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

The Role We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable ...

Machine Learning Engineer I

San Francisco, CA ยท On-site

$151K - $189K/yr

Develop and iterate on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization -- with guidance from senior engineers.

The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving ... Machine Learning Manager In order to execute our vision, we're constantly growing our machine ...

Machine Learning Manager

San Francisco, CA ยท On-site

$180K - $250K/yr

The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving ... Machine Learning Manager In order to execute our vision, we're constantly growing our machine ...

We invite you to help us build that future. (See how people use Elicit today on Twitter; explore our vision in the roadmap.) About the role As a Machine Learning Engineer at Elicit, you'll build ...

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Machine Learning Biomedical Engineer information

See Berkeley, CA salary details

$38.6K

$157.7K

$236.9K

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

As of Jun 28, 2026, the average yearly pay for machine learning biomedical engineer in Berkeley, CA is $157,670.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,300.00 and $189,800.00 per year, depending on experience, location, and employer.

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.

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 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.
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