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What you’ll do About the internship Stripe's Applied ML, Data Science, Risk, and Payments organizations are excited to offer PhD machine learning engineering internships for the summer of 2026. 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. Who you are Minimum requirements 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 2026 or spring/summer 2027. Practical experience with programming and machine learning, evidenced by projects, classwork, or research. Familiarity with languages such as Python, Scala, Spark and libraries such as Pandas, NumPy, and Scikit-learn. Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models 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. Intent to return to the degree-program after the completion of the internship/co-op. Preferred qualifications You Might Also Have: Two years of university education or equivalent experience, with in-depth knowledge in specific domains of machine learning. Published and presented peer-reviewed articles in top-tier venues. 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. Application requirements 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. In-office expectations Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in Stripe Delivery Center roles in Mexico City, Mexico and Bengaluru, India work 100% from the office. Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss. This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility when possible. Pay and benefits The annual US base salary range for this role is $123,500 - $182,000. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process. Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. Office locations South San Francisco HQ, New York, or Seattle Team University Job type Intern Apply for this role Please find our California applicant personal information notice here . We look forward to hearing from you At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us. 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Launched out of Y Combinator’s 2009 Summer batch, Stripe is a global technology company that builds economic infrastructure for the internet. Businesses of every size—from new startups to public companies—use our software to accept payments and manage their businesses online Stripe is a proud partner of YC companies—from Airbnb (S09) to Defog (W23)—to help them grow their businesses and increase the GDP of the internet.
Salary
$45,000 - $115,000
Location
San Francisco, New York City, Seattle