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Fall Machine Learning Co Op Jobs in Berkeley, CA

Jobzhr

Machine Learning Engineer, Drive

San Francisco, CA · On-site

$204 - $299/hr

The Drive Machine Learning team builds the prediction and intelligence systems that power this ... Compensation The successful candidate's starting pay will fall within the pay range listed below ...

Waabi

Research Internship/Co-op

San Francisco, CA

$45 - $60/hr

Qualifications: - Bachelors or MS/PhD degree in Computer Science, Engineering, AI, Machine Learning ... Application Instructions: - To be considered for an internship/co-op, please add your most up to ...

Waabi

Research Internship/Co-op

San Francisco, CA · On-site +1

$45 - $60/hr

Qualifications: - Bachelors or MS/PhD degree in Computer Science, Engineering, AI, Machine Learning ... Application Instructions: - To be considered for an internship/co-op, please add your most up to ...

LiveRamp

Co-Op, DevOps Engineer

San Francisco, CA · On-site

$62.25 - $85/hr

Get ready for the best learning experience of your life. You will receive the same orientation ... Co-Op DevOps Engineer LiveRamp is the data collaboration platform of choice for the world's most ...

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All JobsFall Machine Learning Co Op JobsFall Machine Learning Co Op Jobs in Berkeley, CA

Fall Machine Learning Co Op information

See Berkeley, CA salary details

$31.2K

$52.1K

$107.8K

How much do fall machine learning co op jobs pay per year?

As of Aug 21, 2026, the average yearly pay for fall machine learning co op 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 Fall Machine Learning Co Op?

A Fall Machine Learning Co-Op is a temporary, typically full-time position for students or recent graduates to gain hands-on experience in applying machine learning techniques. These roles usually involve working with data, training models, and optimizing algorithms under the supervision of experienced engineers or researchers. They are offered during the fall semester and can last several months. Companies use these positions to provide practical learning opportunities and assess potential future hires.

What can I expect from the day-to-day experience of a Fall Machine Learning Co Op?

As a Fall Machine Learning Co Op, you'll typically work with a team of data scientists and engineers on real projects that may involve data cleaning, model development, testing, and reporting insights. Your days might include collaborating in meetings, coding, analyzing data, and presenting findings to team members or supervisors. You'll receive mentorship from experienced professionals and have opportunities to participate in code reviews and brainstorming sessions. This structure helps you build technical skills, broaden your professional network, and gain a comprehensive understanding of how machine learning is applied in a business setting.

What are the key skills and qualifications needed to thrive in the Fall Machine Learning Co Op position, and why are they important?

To thrive as a Fall Machine Learning Co Op, you should have a solid background in programming (especially Python), statistics, and machine learning concepts, often supported by coursework or hands-on projects in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, and data analysis libraries such as pandas and scikit-learn is highly valued, while certifications in AI or data science can be a plus. Strong problem-solving skills, eagerness to learn, effective communication, and teamwork help you stand out in this role. These skills are crucial for contributing to real-world projects, collaborating with technical teams, and gaining valuable experience in a fast-paced, innovation-driven environment.

What are popular job titles related to Fall Machine Learning Co Op jobs in Berkeley, CA?

For Fall Machine Learning Co Op jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Fall Machine Learning Co Op jobs in Berkeley, CA look for?

The top searched job categories for Fall Machine Learning Co Op jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Fall Machine Learning Co Op jobs?

Cities near Berkeley, CA with the most Fall Machine Learning Co Op job openings:

Fall Machine Learning Co Op jobs near you
Infographic showing various Fall Machine Learning Co Op job openings in Berkeley, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $52,141 per year, or $25.1 per hour.

Co-Op, ML Scientist for Biology

Lila Sciences

San Francisco, CA • On-site

Full-time

Re-posted 27 days ago

Job description

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve hard problems across scientific domains. Within Life Sciences AI, we are developing autonomous-science capabilities for biological systems, spanning multiple biological domains and resolutions, based on multi-modal data and foundation models.

We are seeking a Co-Op, LS AI, ML Scientist for Biology to contribute to cutting-edge research on how to effectively evaluate, guide, and reinforce agentic model behavior in this domain.

This is an opportunity to work alongside Lila scientists on early-stage research in autonomous life science AI. You will help explore reasoning models, evaluation and benchmark datasets, and workflows that connect modern AI methods to real biological questions, gaining hands-on experience in a fast-moving scientific environment.

What You'll Be Building

  • Contribute to ML research on reasoning models for biological discovery and autonomous science.
  • Explore methods to evaluate, guide, and reinforce agentic model behavior in biological domains.
  • Help develop evaluation and benchmark datasets for biological reasoning tasks.
  • Analyze multi-modal biological data to identify useful signals for model evaluation and improvement.
  • Prototype workflows that connect model reasoning, evaluation, and scientific feedback.
  • Communicate findings through code, notebooks, written summaries, and presentations.

What You'll Need to Succeed

  • Currently enrolled in a PhD program in Computer Science, Machine Learning, Computational Biology, Bioengineering, or a related quantitative field.
  • Research experience in machine learning, AI for science, computational biology, or biological data analysis.
  • Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
  • Experience working with biological, scientific, or multi-modal datasets.
  • Interest in reasoning models, agentic systems, evaluation methods, or benchmark design.
  • Interest in closed-loop scientific discovery, autonomous labs, or AI systems that interact with experimental feedback.
  • Ability to communicate research findings clearly through code, notebooks, written summaries, and presentations.
  • Comfort working in a collaborative, cross-disciplinary research environment.

Bonus Points For

  • Experience with reasoning models, agentic systems, reinforcement learning, or model evaluation.
  • Experience developing benchmarks, evaluation datasets, or model assessment workflows.
  • Publications, preprints, talks, posters, or workshop presentations in ML, AI for science, computational biology, or related scientific venues.

Apply