Mastercard
All Teams
AI and Data
At Mastercard, our AI and Data teams set direction, build shared capabilities, and work side-by-side with cross-functional teams to scale AI and data responsibly across the organization. They help solve challenges that cut across products, regions, and customer needs. Examples include: - building AI systems that support safer, smarter, and more personal commerce - developing shared data platforms and lakehouse capabilities to accelerate innovation - improving data discoverability, quality, and lifecycle management - advancing AI deployment, monitoring, and operations at enterprise scale - supporting new AI ideas through a structured AI intake and prioritization process
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Mastercard Employee Perspectives
What People Are Saying About Mastercard
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Values & Integrity: AI Engineering operates under explicit Responsible AI guardrails (governance councils, privacy/fairness checks) and a stated culture of decency. Feedback suggests these norms provide clarity and trust when building and shipping production systems.
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Learning & Development: Engineers have access to AI literacy programs, guilds/collectives, and the Unlocked internal marketplace for projects, mentors, and rotations. Feedback suggests this creates visible pathways to upskill and explore adjacent domains without leaving the team.
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Innovation & Products: Structures like AI Garage, Foundry, and Test & Learn encourage experimentation that leads to production-grade platforms and features. Feedback suggests AI engineers can prototype within guardrails and see work land at enterprise scale.
Recently posted jobs
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Lead delivery of user stories for Certificate and Privileged Access Management platforms: refine requirements, design, code, test, and implement solutions; provide technical analysis, support, and subject-matter expertise; participate in reviews, CI/CD, and cross-team integration.
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
Design, build, and deploy production AI solutions including transformer-based and generative models. Build data pipelines, training workflows, model serving and inference, apply MLOps practices, collaborate with product and engineering teams, and monitor and tune models in production.
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
Design, build, and deploy production AI solutions: fine-tune and evaluate transformer and generative models, implement training and inference workflows, build data pipelines and feature engineering, apply MLOps practices, integrate model serving into APIs, monitor and tune production models, and collaborate with product and engineering teams to deliver reliable AI systems.
