Machine Learning Software Engineer Intern – Winter 2027

Job Description

Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365-all within 90 seconds.
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 Winter 2027 (Middle of January-April) 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. 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. You will 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.
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.
Passion to learn and develop your skills, both in machine learning and software engineering.
Experience with developing things that use large language models (LLMs) and familiarity with pre-training and fine-tuning techniques.

Here at Rippling, we have many teams that span across all different types of work varying from product development, platform enhancement, and infrastructure strengthening. The machine learning team works with all of our different teams.

Our various product teams have a combination of both directly user-facing products as well as interfaces for other engineering teams. There are challenges for the team that range from building for usability to scaling for massive growth and a wide array of engineering requirements.

Rippling’s platform engineering teams provide a layer of abstractions for every product team to build quickly and scale effortlessly. Providing a strong platform layer enables Rippling to build as a startup of startups. These teams are responsible for building the foundational software that powers the Rippling platform.

Infrastructure type teams lead architecture initiatives that support Rippling’s continued scalability and performance through a period of hyper-growth.

We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a 40 mile radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee’s role.