Overview
On Site
Full Time
Skills
Natural language processing
Machine Learning (ML)
Internet
Financial services
Computer science
Unsupervised learning
Collaboration
Programming languages
GDP
Operations
Finance
Fraud
Data
Creativity
Training
Spring Framework
Software development
Research
Ruby
JavaScript
Scala
Management
Writing
Communication
Articulate
LinkedIn
GitHub
Commerce
IMPACT
Job Details
Who we are
About Stripe
Stripe's mission is to increase the GDP of the internet, and we empower businesses of every size to start, run, and scale their operations with world-class financial tools. Our work impacts millions of users around the globe, and we're looking for applied researchers to join our team.
What you'll do
About the internship
Stripe's Applied ML organization is excited to offer PhD machine learning engineering internships for the summer of 2024. This is an exceptional opportunity to contribute to critical projects that directly enhance Stripe's suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.
As an intern, you'll tackle challenging problems at the intersection of finance, technology, and data. You'll have the chance to work on creative projects like the Stripe Assistant and the Stripe Foundation Model, which leverage machine learning to revolutionize how businesses interact with financial services and data.
Responsibilities
Minimum requirements
We're Looking for Someone Who Has:
You Might Also Have:
Please submit the following with your application:
Join us for an unforgettable summer internship and help shape the future of global commerce. At Stripe, you won't just be working on theoretical projects; you'll make a tangible impact on the world's economic infrastructure.
Hybrid work at Stripe
Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.
About Stripe
Stripe's mission is to increase the GDP of the internet, and we empower businesses of every size to start, run, and scale their operations with world-class financial tools. Our work impacts millions of users around the globe, and we're looking for applied researchers to join our team.
What you'll do
About the internship
Stripe's Applied ML organization is excited to offer PhD machine learning engineering internships for the summer of 2024. This is an exceptional opportunity to contribute to critical projects that directly enhance Stripe's suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.
As an intern, you'll tackle challenging problems at the intersection of finance, technology, and data. You'll have the chance to work on creative projects like the Stripe Assistant and the Stripe Foundation Model, which leverage machine learning to revolutionize how businesses interact with financial services and data.
Responsibilities
- Develop and deploy large-scale machine learning systems that drive significant business value across various domains.
- Engage in the end-to-end process of designing, training, improving, and launching machine learning models.
- Write production-scale ML models that will be deployed to help Stripe enable economic infrastructure access for a diverse range of businesses globally.
- Collaborate across teams to incorporate feedback and proactively seek solutions to challenges.
- Rapidly learn new technologies and approaches, demonstrating a strong ability to ask insightful questions and communicate the status of your work effectively.
Minimum requirements
We're Looking for Someone Who Has:
- A deep understanding of computer science, obtained through the pursuit of a PhD in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2024 or spring/summer 2025.
- Practical experience with programming and machine learning, evidenced by projects, classwork, or research. Familiarity with languages such as Ruby, JavaScript, Scala, and Go is beneficial, but not required.
- Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML Operations, and possibly experience in Natural Language Processing or reinforcement learning.
- Demonstrated ability to work on collaborative projects, with experience in receiving and applying feedback from various stakeholders.
- A proactive approach to learning unfamiliar systems and a demonstrated ability to understand complex systems independently.
You Might Also Have:
- Two years of university education or equivalent experience, with in-depth knowledge in specific domains of machine learning.
- Experience in writing high-quality pull requests, maintaining good test coverage, and completing projects with minimal defects.
- Familiarity with navigating new codebases and managing work across different programming languages.
- Excellent written communication skills to clearly articulate your work to both team members and wider Stripe audiences.
Please submit the following with your application:
- A detailed resume or LinkedIn profile showcasing your work history.
- Examples of relevant work and your approach to learning, such as GitHub repositories, StackOverflow contributions, or other project portfolios.
Join us for an unforgettable summer internship and help shape the future of global commerce. At Stripe, you won't just be working on theoretical projects; you'll make a tangible impact on the world's economic infrastructure.
Hybrid work at Stripe
Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.