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Remote Undergraduate Machine Learning Internship Jobs in San Jose, CA

Waymo

Machine Learning Engineer - Mapping

Mountain View, CA · On-site +1

$204K - $259K/yr

Machine Learning Engineer - Mapping Waymo is an autonomous driving technology company with the ... remote, the specific salary range for your preferred location, during the hiring process. Waymo ...

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How much do remote undergraduate machine learning internship jobs pay per year?

As of May 28, 2026, the average yearly pay for remote undergraduate machine learning internship in San Jose, CA is $49,908.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,100.00 and $53,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Undergraduate Machine Learning Intern, and why are they important?

To thrive as a Remote Undergraduate Machine Learning Intern, you generally need a foundation in programming (typically Python), statistics, linear algebra, and coursework in machine learning or data science. Familiarity with technical tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems like Git is often expected. Strong problem-solving skills, self-motivation, and effective remote communication set top candidates apart. These competencies ensure interns can contribute to projects independently, collaborate virtually with teams, and adapt quickly to the evolving demands of machine learning tasks.

What types of projects and team collaborations can I expect during a remote undergraduate machine learning internship?

As a remote undergraduate machine learning intern, you can expect to work on projects such as data preprocessing, model development, and algorithm evaluation under the guidance of experienced mentors. Collaboration often happens through virtual meetings, code reviews, and shared platforms like GitHub or Jupyter Notebooks. You'll likely be paired with a team of data scientists or engineers and may contribute to both ongoing research and practical applications. Regular check-ins and feedback sessions help ensure your learning and integration into the team, fostering both technical growth and communication skills.

What is a Remote Undergraduate Machine Learning Internship?

A Remote Undergraduate Machine Learning Internship is a temporary, typically part-time position for undergraduate students to gain hands-on experience in machine learning while working from a location outside of a traditional office, such as their home. Interns work on real-world projects involving data analysis, model development, and coding under the supervision of experienced professionals. These internships help students apply academic knowledge, develop technical and soft skills, and build their resumes for future job opportunities in the field of artificial intelligence and data science.
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Remote Undergraduate Machine Learning Internship jobs near you

Machine Learning Engineer, RL Environments - Internship

Preference Model

San Francisco, CA • On-site, Remote

Other

This job post has expired today. Applications are no longer accepted.

Job description

Preference Model Internship

Location: San Francisco preferred, remote considered

Compensation: Paid internship

About Us

Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.

Our founding team has previous experience on Anthropic's data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

About the Role

We're looking for PhD or Master's students, and gifted undergrads to spend an internship with us working on building RL training environments for large language models.

This role blends research and engineering. It will require you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers and engineers.

What You'll Do

  • Design and build RL environments that test LLM reasoning on ML, systems, and research problems
  • Write clean, production-grade Python (not notebooks)
  • Work with Docker, build reproducible environments, debug when things break
  • Translate ML papers and concepts into concrete training tasks

What We Are Looking For (Qualifications)

You're an undergrad or PhD student in CS, ML, math, physics, or a related field. You write real code, not just research prototypes. You read ML papers for fun in your free time.

Must Have

  • Strong Python skills
  • Familiarity with how LLMs work, what they're good at, and where they fall short
  • Ability to work independently, take feedback, and iterate fast

You May Be a Good Fit If One of the Following Applies

  • You understand transformer internals and have worked with training or inference code
  • You've written CUDA kernels or worked with low-level GPU programming
  • You have a research area you know deeply (publications, public code, or strong coursework)
  • You read broadly across ML and can connect ideas from different subfields
  • You've built interactive environments, simulations, or complex software systems

What We Offer

  • Paid internship with opportunity to return full time based on performance
  • Ownership and autonomy in a fast moving startup environment
  • Opportunity to work with top machine learning engineers Competitive cash and equity compensation (>90th percentile)
  • Lunch provided everyday onsite
  • Weekly snack orders

Note: We utilize AI note-taking during our interview sessions to ensure we capture all answers and details accurately. Candidates are allowed to use AI note-takers as well, however, no other AI tools are permitted during any live interviews.

Apply