ZipRecruiter

Log In

1

Machine Learning Biomedical Internship Jobs in Berkeley, CA

Capital One

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... • Strong background in machine learning engineering with a focus on model optimization ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... • Strong background in machine learning engineering with a focus on model optimization ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA

$144K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

What we're looking for * 4+ years of non-internship professional MLE experience. * Deep expertise ... Strong background in machine learning engineering with a focus on model optimization, distillation ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

What we're looking for * 4+ years of non-internship professional MLE experience. * Deep expertise ... Strong background in machine learning engineering with a focus on model optimization, distillation ...

Atoms

Staff Machine Learning Engineer

San Francisco, CA

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

What we're looking for * 10+ years of non-internship professional MLE experience. * Deep expertise ... Strong background in machine learning engineering with a focus on model optimization, distillation ...

previous pagenext page

Showing results 21-40

All JobsMachine Learning Biomedical JobsMachine Learning Biomedical Internship JobsMachine Learning Biomedical Internship Jobs in Berkeley, CA

Machine Learning Biomedical Internship information

See Berkeley, CA salary details

$31.2K

$52.1K

$107.8K

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

As of Aug 19, 2026, the average yearly pay for machine learning biomedical internship in Berkeley, CA is $52,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,800.00 and $56,300.00 per year, depending on experience, location, and employer.

What is a machine learning biomedical internship?

A Machine Learning Biomedical Internship is a temporary position where students or recent graduates work with professionals to apply machine learning techniques in the biomedical field. Interns typically assist with data analysis, model development, and research projects that involve biological or medical data. The goal is to gain practical experience in using artificial intelligence to solve healthcare challenges, such as disease prediction, medical imaging, or drug discovery. These internships often require knowledge of programming languages like Python and familiarity with machine learning frameworks. They provide valuable hands-on experience and networking opportunities for those interested in biomedical data science careers.

What types of projects do interns typically work on during a machine learning biomedical internship?

Interns in Machine Learning Biomedical roles often contribute to projects involving the development and validation of algorithms for analyzing medical data, such as imaging, genomics, or electronic health records. They may assist with data preprocessing, model training, and performance evaluation under the guidance of experienced researchers or engineers. Collaboration is common, as interns often work closely with interdisciplinary teams including data scientists, clinicians, and software engineers. This hands-on experience provides valuable exposure to real-world biomedical challenges while strengthening both technical and communication skills.

What are the key skills and qualifications needed to thrive as a machine learning biomedical intern, and why are they important?

To excel as a Machine Learning Biomedical Intern, you need a solid background in computer science, statistics, and biology, often supported by coursework or a degree in related fields. Familiarity with programming languages like Python or R, experience with machine learning libraries (such as TensorFlow or scikit-learn), and knowledge of data analysis tools are typically required. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical concepts clearly are crucial soft skills. These competencies enable interns to develop effective models, collaborate with multidisciplinary teams, and contribute meaningful insights to biomedical research projects.

What are popular job titles related to Machine Learning Biomedical Internship jobs in Berkeley, CA?

For Machine Learning Biomedical Internship jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Biomedical Internship jobs in Berkeley, CA look for?

The top searched job categories for Machine Learning Biomedical Internship jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Machine Learning Biomedical Internship jobs?

Cities near Berkeley, CA with the most Machine Learning Biomedical Internship job openings:

Machine Learning Biomedical Internship jobs near you

Founding Machine Learning Engineer

Orbit Neuro

San Francisco, CA • On-site

$225K - $275K/yr

Full-time

Re-posted 28 days ago

Job description

About the company
We're a team of engineers, neuroscientists, and designers solving the most difficult and meaningful challenge: understanding the human brain. Our translational brain computer interface and pioneering models decode emotion, putting experience and wellbeing at the center of every interaction.
Our wearable BCI achieves fMRI-comparable resolution untethered to the lab. It's this advancement that enables us to build foundation models of emotion.
We're looking for people to help us build and scale. If you want to work on deep technology with real impact, and help define the future of brain-computer interfaces and AI, join us.
We're backed by the founders and execs of the leading companies in AI, neurotech, consumer hardware and pharmaceuticals - including Google, Hugging Face, Apple, Stability, Microsoft and Dropbox. We're venture funded.
About the team we are building
We're building a generational founding team which is truly full-stack - from neural sensors to complex models. If you want to work on deep technological problems and help pioneer the future of NeuroAI, this is the place for you. Projects have opportunities for a high degree of autonomy and demand intense, fast-paced learning.
You will:
  • Critically evaluate and implement the best machine learning approaches for our unique design problems in neural data
  • Work with real-time, multi-dimensional, multimodal datasets
  • Collaborate closely with neuroscience, hardware, and software teams to co-design end-to-end systems
  • Explore new model architectures and perform detailed experimentation and analysis
  • Learn neuroimaging and neuroscience context (we will support you in getting up to speed)
You have:
  • An BS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or a related STEM field (exceptional self-taught researchers also considered)
  • 3+ years of applied ML research or development experience, or equivalent depth through publications, projects, or startup work
  • Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX
  • Built and iterated quickly on ML models and pipelines
  • Experience with data preprocessing, labeling, and exploratory analysis
  • Agility working with multimodal data (e.g., imaging + time series, text + audio)
  • Proven ability to thrive in small, fast-moving teams
You might also have:
  • Publications in top ML or domain-specific journals/conferences
  • Experience with biomedical, neuroimaging, or other high-dimensional sensor data
  • A background in signal processing for time-series or imaging data
  • Experience with distributed or large-scale training (e.g., mixed precision, very large datasets)
  • Knowledge of semi-supervised or self-supervised approaches
  • Excitement to learn neuroimaging and neuroscience context

Apply