Machine Learning Software Engineer Intern - Summer 2027

RipplingSan Francisco, CA
Onsite

About The Position

At Rippling, Engineering is at the heart of our business and culture. We believe in our early talent’s potential, experience and aspirations. Our mission is to open the realm of possibilities and opportunities for students by fostering growth through mentorship and training, ownership through impactful and innovative projects, and, of course, fun through our diverse culture and work hard, play harder attitude. As an Engineering Intern, you will join the Machine Learning Team in Summer 2027 (May/June - August/September) to develop robust, well-designed products, implement new updates and features, and solve complex problems that affect our business and our clients. Rippling Interns gain the experience of full-time engineers with responsibilities, ownership, and opportunity. Interns will be assigned projects that not only have an impact on the business, but that are also scoped to fit within the time constraints of their internship so they can see the full impact of their work. We provide our interns exciting and innovative projects to work on that allow them to see the fruition of their work. During your 13 weeks at Rippling, we will provide all of the tools needed to be successful. Your dedicated mentor and manager will provide support for your learning and development throughout your time with us. You will join your fellow interns in a variety of socials, talks with Rippling leaders, and more!

Requirements

  • Currently enrolled in a M.Sc. or Ph.D. program in computer science or in a related field during the course of the internship.
  • Solid programming skills with an emphasis on backend experience and knowledge.
  • Our production code base is primarily Python, PySpark, and PyTorch.
  • Excellent communication skills.
  • Passion to learn and develop your skills, both in machine learning and software engineering.

Nice To Haves

  • Experience with developing things that use large language models (LLMs) and familiarity with pre-training and fine-tuning techniques.

Responsibilities

  • Translate product needs into crisp mathematical formulations (models); train those models; and, with a small team ship them to production.
  • Learn how to design scalable machine learning pipelines for data preprocessing, feature engineering, model training, and evaluation.
  • Work with data engineers to collect and preprocess data sets for model training.
  • Stay up-to-date with the latest research in ML and related fields, and apply this knowledge to improve Rippling products.
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