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Machine Learning Biomedical Internship Jobs in Berkeley, CA

Crowe LLP

Machine Learning Intern

San Francisco, CA · On-site

$27 - $42/hr

DS interns will have a designated data scientist mentor and will spend the majority of their time working on a data science project overseen by that mentor. The expectation is that at the end of the ...

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 ...

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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 Sep 8, 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?

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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

Machine Learning Engineer

San Mateo, CA

BigHat Biosciences
Biotechnology Research and Development • 11 - 50 employees
Learn more about BigHat Biosciencesexternal

$150K - $200K/yr

Full-time

Posted 4 days ago

Job description

The role: We are seeking a creative, ambitious Machine Learning Scientist or Engineer to advance the state of the art in ML-driven therapeutic antibody design.

At BigHat Biosciences our full-stack antibody drug development platform uses AI/ML to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS++ data management and orchestration layer to automatically update and deploy the latest models. This makes development of complex, net-gen therapeutics 'trivially parallelizable', at a pace which only accelerates as we develop better ML tooling.

You're not interested in just git-cloning the latest NeurIPS pub and swapping out the dataset. Motivated by an enthusiasm for the possibility of addressing unmet patient need, and a curiosity about the underlying biology, you'll apply your top-tier ML skillset to refine and expand this state of the art protein engineering platform. Success will mean not only hands-on methods development, but actively participating in the application of our platform to the accelerated design of new drugs for devastating diseases.

  • Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
  • Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
  • Develop, refine, and deploy agentic and LLM-driven optimization methods to further automate and accelerate our design-build-test loop.
  • Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
  • Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models and methods.
  • Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. - every therapeutics program at BigHat is heavily interdisciplinary.
  • Masters in ML/CS/EE or Bachelors with 3+ years industry experience; hands on experience developing and applying novel ML methods and a strong quantitative background.
  • Strong competency in Python, familiarity with PyTorch (even without LLMs!) and experience with modern software engineering best practices, including not just agentic/LLM-assisted coding but testing, CI/CD, etc.
  • Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
  • Energy and ambition - ready to dive into a fast-paced environment and execute across multiple projects.
  • Familiarity with the current state-of-the-art in ML-driven protein engineering
  • Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, experience training and deploying models on AWS, and publications at major ML conferences.
The salary estimated for this position is $150,000 - $200,000 + bonus + options + benefits. Compensation will vary depending on job-related knowledge, skills, and experience. Actual compensation will be confirmed in writing at the time of the offer.

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