Ibotta is seeking a Machine Learning Intern to join our innovative team and contribute to our mission to Make Every Purchase Rewarding. As a participant in our 2025 Summer Internship, you will have the opportunity to fully integrate with your team to directly contribute to ongoing Ibotta business initiatives. In addition to the daily time with your team, you will participate in ongoing teach outs curated to help build skills essential to transitioning into the workforce and building your career. We are looking for candidates who are eager to learn, excited to work on real-world business challenges, and want to be part of a mission-driven company.
This will be a full-time, 12 week, internship during the summer of 2025. This is a hybrid position located in Denver, Colorado and requires 3 days in-office per week. Required in-office hybrid days are Tuesday, Wednesday, and Thursday. Candidates must live in the United States.
What you will be doing:
Work with the mentor on understanding our models, users and clients.
Build, train, and validate deep learning, recommender, and reinforcement learning
Run statistical analysis and create predictive models based on past user purchases and behavior
Develop recommender systems and machine learning algorithms for user targeting and personalization
Build well tested, maintainable, and extendable code
Report and understand system performance in detail, and identify ideas for improvement
Embrace and uphold Ibotta’s Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency & A good idea can come from anywhere
What we are looking for:
Juniors working towards a bachelor’s degree with a focus in Computer Science, Engineering, Data Analytics or a related field
Proven ability to think creatively and implement ideas from start to finish
Good written and verbal communication skills
Hunger to learn and collaborate with your teammates
Good understanding of the key concepts of Recommender Systems and Machine Learning
Proven expertise with data handling, processing, statistical and analytical skills
Python experience is required, as well as experience with either Tensorflow or PyTorch
Spark/PySpark experience is an additional plus
Ability to develop and maintain ML models, in addition to being able to visualize, analyze, and communicate results
Experience with SQL required
Good understanding of A/B testing and control group concepts
About Ibotta ("I bought a...")
Ibotta (NYSE: IBTA) is a leading performance marketing platform allowing brands to deliver digital promotions to over 200 million consumers through a network of publishers called the Ibotta Performance Network (IPN). The IPN allows marketers to influence what people buy, and where and how often they shop – all while paying only when their campaigns directly result in a sale. American shoppers have earned over $1.8 billion through the IPN since 2012. The largest tech IPO in history to come out of Colorado, Ibotta is headquartered in Denver, and is continually listed as a top place to work by The Denver Post and Inc. Magazine.
To learn more about what our Tech teams are doing day to day, visit Building Ibotta on Medium.com
Additional Details:
This position is located in Denver, CO and includes competitive pay and housing assistance for out of state students.
The hourly compensation for this role is: $35.77 per hour worked. This compensation range is specific to the United States labor market.
Ibotta is an Equal Opportunity Employer. Ibotta’s employment decisions are made without regard with race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected status.
Applicants must be currently authorized to work in the United States on a full-time basis.
For the security of our employees and the business, all employees are responsible for the secure handling of data in accordance with our security policies, identifying and reporting phishing attempts, as well as reporting security incidents to the proper channels.
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