Principal Software Engineer

Mastercard • Arlington, VA

21d • Hybrid

About The Position

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.

As a Principal Engineer within the Decision Stream program, you will combine enterprise-scale technical leadership with hands-on engineering for the next-generation Decision Management Platform. This is not a strategy-only role. You will actively design, code, prototype, and validate core platform capabilities, using modern AI-assisted development tools as part of day-to-day software engineering to move faster, improve quality, and help teams adopt better ways of building. Key areas of focus include leveraging disruptive technologies in real-time AI inferencing and decisioning to improve product effectiveness, increase business delivery, strengthen technical resilience, and lower cost of ownership. You will work closely with technology executives, senior leaders, and engineers to shape the overall AI & DPE technology strategy.

Requirements

• Extensive experience in software engineering and technical leadership
• Proven delivery of large-scale distributed systems
• Expertise in cloud, AI/data platforms, and modern engineering practices
• Strong communication and mentorship skills
• Bachelor’s degree (or equivalent); advanced degree preferred
• Telecommuting and/or working from home may be permissible pursuant to company policies.
• Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role.
• In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact [email protected] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.
• 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.

Nice To Haves

• AI-native engineer — uses AI-assisted development as default
• Innovation leader — builds systems at massive scale and availability
• Streaming-first mindset — experience with low-latency pipelines
• Proven outcomes — delivers impactful, production-ready systems
• Engineering culture champion — drives best practices and transparency
• Collaborative — works across engineering and data science teams
• Data architectures for decisioning: lakehouses, delta lakes, distributed logs, and product-aligned data models.
• Feature catalogs and engineering platforms for reusable, governed features that can be defined, discovered, validated, and served across batch and real-time decisioning.
• Decisioning data models covering events, derived features, reference data, labels, outcomes, and policy data consumed by rules, models, and agentic workflows.
• Data contracts, lineage, freshness, and quality controls that keep decisioning data trustworthy, explainable, and production ready.
• Event streaming and high-throughput data pipelines.
• Low-latency data technologies -distributed caches, in-memory data grids.
• Real-time transaction processing and sub-second decisioning.
• ML lifecycle engineering: model training, deployment, refresh, and low-latency inference.
• Agentic AI patterns, LLM integration, and prompt engineering applied to platform and product problems.
• Model observability, drift detection, and feedback loops that keep AI systems reliable in production.
• Authoring, testing, and deployment of business rules engine rules and similar decisioning logic.
• Tooling that helps authors validate rules, models, and policies pre-deployment.
• Operator experience for authors, analysts, and SREs: lifecycle, approvals, observability, and explainability of live decisions.
• Rules engine design and modernization.
• AWS infrastructure engineering and cloud-native platform patterns.
• DevOps and platform engineering -automation, CI/CD, observability, GitOps.

Responsibilities

• Build software, tooling, and platform capabilities.
• Design and implement large scale distributed systems.
• Develop reusable services, patterns, and integrations.
• Contribute to new product and prototype development from concept through validation.
• Evaluate systems, frameworks, and tools across quality, cost, latency, scalability, reliability , and maintainability.
• Apply sound engineering judgment to trade-offs in distributed systems design and architecture.
• Apply AI tools as part of daily engineering practice to real product and platform problems.
• Teach and model the adoption of AI-assisted development, modern languages, and current engineering practices.
• Improve developer experience through automation, AI-assisted workflows, and platform thinking.
• Advocate learnings, prototypes, and best practices across the organization.
• Own and improve end-to-end customer experience across a portfolio of services and applications.
• Simplify and optimize architecture strategies to balance cost, performance, and business value.
• Apply judgment and experience to make trade-offs between competing priorities and technical constraints.
• Lead architectural design for complex, enterprise-wide initiatives spanning multiple services and programs.
• Drive organization-wide initiatives to advance software engineering craftsmanship and best practices.
• Represent the organization through public speaking, technical blogs, and white papers on emerging technologies.
• Participate in Principle-level architecture reviews and resolve enterprise-wide technical and regulatory challenges.
• Mentor engineers at all levels, fostering technical growth and leadership.
• Conduct technical interviews to raise the performance bar and attract top talent.
• Provide unbiased, accomplishment-based recommendations for promotions.
• Champion AI-assisted engineering as a default working practice and align it with organizational values.

Benefits

• insurance (including medical, prescription drug, dental, vision, disability, life insurance)
• flexible spending account and health savings account
• 16 weeks of new parent leave
• up to 20 days of bereavement leave
• 80 hours of Paid Sick and Safe Time
• 25 days of vacation time
• 5 personal days
• 10 annual paid U.S. observed holidays
• 401k with a best-in-class company match
• deferred compensation for eligible roles
• fitness reimbursement or on-site fitness facilities
• eligibility for tuition reimbursement
• 56 hours of Paid Sick and Safe Time (for interns)
• jury duty leave (for interns)
• on-site fitness facilities in some locations (for interns)

What This Job Offers

• Job Type: Full-time
• Career Level: Principal