Lead Deep Learning Engineer

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

Lead Deep Learning Engineer

Overview
We are seeking a highly skilled Lead Deep Learning Engineer (L6) with strong expertise in deep learning architecture design and the ability to train models from scratch, including transformers, graph neural networks (GNNs), sequence models, and generative architectures.
In this role, you will lead the development of innovative, scalable AI systems that power Mastercard’s products and platforms. You will collaborate closely with product, engineering, Legal, and AI Governance teams to translate complex business problems into advanced machine learning solutions that are production‑ready and compliant with responsible AI standards.
This position is ideal for someone who thrives at the intersection of research and engineering, driving both experimentation and real‑world deployment across Mastercard’s global ecosystem.
Responsibilities:
Deep Learning Research & Development
• Design, implement, and train custom deep learning models from scratch, example:
• Transformer architectures (encoder-only, decoder-only, seq2seq)
• Graph Neural Networks (GAT, GraphSAGE, message-passing networks)
• Sequence and representation learning models
• Generative models (VAEs, GANs, diffusion models)
• Develop and manage large-scale training pipelines, including distributed training, mixed precision optimization, and model parallelism.
• Conduct rigorous experimentation such as ablation studies, scaling analysis, and hyperparameter optimization to push model performance boundaries.
• Build reusable internal frameworks for training, evaluation, and model lifecycle management.
Advanced Machine Learning Engineering
• Develop and optimize end-to-end ML systems, including data processing, feature creation, embedding strategy design, retrieval systems, and model deployment.
• Build high performance inference services, optimizing models for latency, cost, reliability, and interpretability.
• Ensure reproducibility, documentation, and model governance across the full ML lifecycle.
• Lead due diligence and quality assurance testing for prototypes, tools, and production systems.
Leadership & Collaboration
• Partner with product managers, engineers, and business stakeholders to translate ambiguous needs into deep learning solutions.
• Provide technical leadership and mentorship to junior data scientists and ML engineers.
• Work closely with O&T, Legal, and AI Governance to ensure models meet Mastercard's responsible AI principles (fairness, transparency, explainability, robustness).
Innovation & Thought Leadership
• Stay current with cutting-edge research in foundation models, multimodal learning, GNNs, causal deep learning, and retrieval-augmented generation.
• Prototype emerging capabilities and assess feasibility for Mastercard's product landscape.
• Drive innovation by identifying opportunities to standardize, automate, and scale model development across the organization.
Key Skills:
Deep Learning Expertise
• Proven experience training deep learning models from scratch using frameworks such as PyTorch, TensorFlow, JAX, or equivalent.
• Strong understanding of modern architectures (transformers, GNNs, generative models), optimization techniques (AdamW, schedulers, regularization), and training stability.
• Hands-on experience with GPU-accelerated computing (A100/H100 or similar), distributed training frameworks (DeepSpeed, PyTorch Distributed, Ray), and model scaling strategies.
Programming & ML Engineering
• Advanced programming skills in Python, and strong practical experience with deep learning and data tooling.
• Familiarity with big data tools and ecosystems (Spark, Hive, Hadoop).
• Analytical & Statistical Foundations
• Deep understanding of advanced statistical concepts, predictive modelling, and machine learning theory.
• Ability to apply quantitative methods to complex business problems and large datasets.
Leadership, Communication & Strategy
• Demonstrated ability to lead cross-functional initiatives, set technical direction, and make architectural decisions.
• Strong communication skills, able to articulate complex technical concepts to technical and non‑technical audiences alike.
Education & Experience
• Master’s or PhD in Computer Science, Machine Learning, Applied Mathematics, or a related field (or equivalent practical experience).
• 8+ years of hands-on experience in machine learning and deep learning, with a track record of building production-grade models.

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.