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Staff Machine Learning Scientist, Intern - Summer 2025

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Visa

2024-10-01 23:41:24

Job location Austin, Texas, United States

Job type: fulltime

Job industry: Science & Technology

Job description

Job Description

Unleash the world scientist within you this summer! Inspired by Richard P. Feynman's maxim, "You do any problem that you can, regardless of field," we're inviting you to contribute your innovative skills in cross-industry advancement of science and humanity at VISA, the technology giant at the forefront of billions of global credit payments. We're calling all physical scientists to venture into our world of ML/AI.

The mission? To uncover fundamental truths hidden in the vast and largely untapped domain of Payment transactions, a realm as complex and intriguing as Physical interactions. Just as scientists construct effective models to make sense of real-world data, we strive to unearth elemental factors driving billions of transactions per day. With the rise of neural network-based deep learning in Physics, we're pioneering the use of these techniques to revolutionize global commerce.

And we're sitting on a gold mine of data and potential applications in the field of payments. As the world transitions from paper and metallic currency to digital payments, the need for solutions in credit, fraud protection, and security is accelerating. With VisaNet, the world's largest fully connected and controlled information network, we're at the forefront of this change, and your background in mathematical modelling, statistics, probability, big data handling, and problem-solving could help shape this future. With $185T in unrealized flows, the opportunity to revolutionize modes of interaction such as C2B, B2B, G2C, and more, awaits you.

Our vast data, the essential simplicity of our base problems, and the rapid advances in ML/AI, especially Generative AI, provide the perfect playground for making breakthroughs in ML/AI, Data Science, and Security. Join us in spearheading innovations in Foundational Machine Learning and leading-edge AI, with ample opportunity to drive patents and papers in top conferences and journals. This summer, turn the world of commerce into your laboratory. Apply for our internship program, and let's reshape the future together!

This position is for a Staff Machine Learning Scientist, Intern with understanding and experience in deep learning, who may be interested in applying deep learning to the world of payments:

You can be part of a team building AI solutions used by billions of Visa cardholders, pushing the boundaries and tradeoffs among accuracy, latency, throughput, and cost.

Or you can apply the breakthroughs of transformers and high-performance computing to financial data Visa has a unique dataset of decades of financial transactions with a unique dataset, we have an opportunity to create unique generative models, that no one else can.

Or you can build tools and processes for experimentation, simulation, testing, and validation of AI models, merging advanced AI techniques with Gen AI capabilities this unique combination not only ensures the observability of AI models, but also enhances their interpretability, paving the way for clear and comprehensive insights.

Or you can help create a forward-looking AI Governance solution to solve new challenges brought by Generative AI, including solutions to issues of AI fairness and explain-ability.

Last, but not the least, you can join the efforts of synthetic data engineering which looks to create an efficient data factory running various learning and generative models for Visa's data at scale while respecting all requirements for security, compliance, and privacy.

Some job duties and projects could include

Collaborate with the team manager of the embedded team to come up with a proposal of work that bridges Deep Learning knowledge with a problem that the team is trying to solve.

Execute the proposal with coding and design in Visa's engineering environment and datacenters, with a demo that showcases the contribution made during the visit.

Evangelize and grow deep learning knowledge within the team and outside based on the work.

Inform a friend!

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