Senior ML Platform Engineer

Posted Yesterday
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Budapest, HUN
Hybrid
Senior level
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
We are a global technology company in the payments industry.
The Role
Build and maintain internal machine learning platforms, distributed data processing solutions, reusable tooling, and large-scale data pipelines. Improve system reliability, performance, automation, and maintainability while collaborating with data scientists and engineers to productionize machine learning solutions. Contribute to architecture and engineering practices, and mentor teammates.
Summary Generated by Built In
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Senior ML Platform Engineer
Our Mission
At Mastercard Identity Verification, we build data, machine learning, and platform capabilities that help customers make safer decisions in digital commerce. Our technologies enable merchants and partners to assess transaction risk, detect fraud, and establish trust using large-scale identity and behavioral signals.
Within the IDV Data Science organization, the Machine Learning Platform (MLP) team develops internal data platforms, engineering foundations, and tooling that support machine learning solutions across our products. We work at the intersection of software engineering, data engineering, distributed computing, and machine learning, transforming large-scale data into production systems that help protect billions of digital interactions every year.
Overview
We are looking for a Senior ML Platform Engineer to join our Budapest team.
This is a hands-on engineering role focused on building, developing, and maintaining internal platforms, tools, and distributed data processing capabilities that enable reliable, production-grade machine learning workflows. The role is centered on software, data, and ML engineering, with platform work meaning the internal capabilities that help Data Scientists and Engineers build, run, and improve machine learning systems at scale.
The ideal candidate is a strong software engineer who enjoys solving complex data and machine learning engineering challenges, writing high-quality code, and building systems that operate reliably at scale.
In This Role You Will
- Build and evolve internal data and machine learning platforms used across IDV Data Science.
- Develop distributed data processing solutions, reusable tooling, and platform capabilities for internal machine learning workflows at scale.
- Improve reliability, performance, automation, and maintainability across the engineering systems and tools we build.
- Collaborate with Data Scientists and engineers to bring machine learning solutions into production.
- Contribute to technical design, architecture, and engineering best practices.
- Mentor teammates and help raise the engineering bar across the organization.
All About You
- Strong software engineering background with professional experience in JVM languages; Scala experience is preferred, but strong Java or Kotlin experience is also welcome where paired with a willingness to learn.
- Solid Python development experience.
- Deep understanding of distributed data processing systems, preferably Apache Spark.
- Experience designing and operating large-scale data pipelines, platforms, or machine learning systems.
- Knowledge of MLOps, Data Engineering, and data warehousing concepts.
- Strong problem-solving skills, a pragmatic engineering mindset, and natural curiosity for learning new technologies and approaches.
- Excellent communication and collaboration skills.
Nice to have:
- Experience with, or interest in, functional programming in a strongly typed setting.
- Databricks experience.
- AWS or other cloud platform experience.
- Experience with modern AI-assisted development tools such as GitHub Copilot, Claude, or similar.
Why Join Us
- Work on large-scale systems that help customers combat fraud and build trust in digital commerce.
- Influence the architecture and direction of internal machine learning platforms and tools used by Data Science teams.
- Collaborate with experienced Data Scientists, Engineers, and Architects.
- Tackle challenging problems involving distributed systems, data, and machine learning at scale.
- Be part of a team that values technical excellence, ownership, curiosity, learning, and continuous improvement.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
  • Abide by Mastercard's security policies and practices;
  • Ensure the confidentiality and integrity of the information being accessed;
  • Report any suspected information security violation or breach, and
  • Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.

Skills Required

  • Professional software engineering experience with JVM languages
  • Scala experience or strong Java or Kotlin experience with willingness to learn Scala
  • Solid Python development experience
  • Deep understanding of distributed data processing systems, preferably Apache Spark
  • Experience designing and operating large-scale data pipelines, platforms, or machine learning systems
  • Knowledge of MLOps, data engineering, and data warehousing concepts
  • Strong problem-solving skills and pragmatic engineering mindset
  • Excellent communication and collaboration skills
  • Functional programming experience or interest in a strongly typed setting
  • Databricks experience
  • AWS or other cloud platform experience
  • Experience with modern AI-assisted development tools such as GitHub Copilot or Claude

What the Team is Saying

Jenny
Mastercard

Mastercard Compensation & Benefits Highlights

  • Retirement Support Retirement plans are presented as best-in-class with a high company match on 401(k) or local equivalents. Career materials and U.S. postings consistently highlight retirement matching as a standout feature.
  • Leave & Time Off Breadth U.S. postings describe generous paid time off including vacation, personal days, holidays, sick/safe time, and additional bereavement leave. A hybrid policy and a limited “work from elsewhere” option further support time away.
  • Parental & Family Support Company pages state a global minimum of 16 weeks of paid new-parent leave across birth, adoption, and foster, plus family-building assistance where permitted. Mental-health resources and caregiving supports are also emphasized.

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The Company
HQ: Purchase, NY
38,800 Employees
Year Founded: 1966

What We Do

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Why Work With Us

We live the Mastercard Way: creating value in the communities we touch, growing together through the opportunities we see, and moving fast to innovate and scale. Our collaborative culture and our passionate people are the key to what we do, driving meaningful change as one team and connecting everyone to priceless possibilities.

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Employees engage in a combination of remote and on-site work.

In our ongoing workplace evolution, we’ve introduced hybrid work, Work-From-Elsewhere Weeks and Meeting-Free Days.

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