MastercardSenior ML Platform Engineer
Mastercard is hiring a Senior ML Platform Engineer in Budapest, Hungary. Level rates it ; you can apply on Mastercard.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- The posting mentions Claude, Copilot and Databricks among its tools and skills.
- Level classes it as a senior role.
- Mastercard has 967 other open roles on Level.
AI in this role
Senior ML Platform Engineer to build and scale internal machine learning platforms, data pipelines, and infrastructure.
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 EngineerOur MissionAt 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.
How we rate this
Senior ML Platform Engineer at Mastercard rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where distributed computing was part of your work. What did you do?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where python was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: ML Ops, Machine learning, Distributed Computing, Data Engineering, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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