Senior Machine Learning Engineer, Wallet, Payment & Commerce

Would you like to contribute to Machine Learning and Generative AI technologies? Are you curious about the data that drives AI/ML success? Do you believe Machine Learning and AI can change the world? We truly believe it can!\\n\\nWe are building the data infrastructure that powers machine learning across Wallet, Payment, and Commerce; and synthetic data is at the center of that strategy.

As a Machine Learning Engineer specializing in Data Synthesis, you will architect privacy-preserving data generation pipelines that reduce dependency on external data procurement, accelerate model development, and set a new standard for responsible ML at scale.\n\nYou'll work at the intersection of cutting-edge generative AI research and production ML systems, collaborating closely with Engineering, Product, Privacy, and Legal teams. This unique opportunity shapes data strategy, impacting features used by millions while pioneering privacy-first ML practices.

BS/Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field, alternatively equivalent industry experience may be considered. \n\n5+ years of experience driving the design and development of machine learning pipelines as an ML Engineer.\n\nHands-on experience building synthetic data generation systems using modern generative techniques (GANs, VAEs, diffusion models, or LLM-based approaches), with measurable impact on model performance or data cost reduction.\n\nHands-on experience synthesizing time series data at scale.\n\nProficiency in Python and relevant ML frameworks (PyTorch, TensorFlow).\n\nProficiency in Spark, Ray, or other distributed computing technologies for developing pipelines at scale.\n\nProficiency in using industry-standard tools and techniques for statistical testing and data experimentation.\n\nExperience with data augmentation across multiple data types (structured, unstructured, and semi-structured).\n\nStrong data exploration and analytical skills, with the ability to assess and characterize diverse data assets.\n\nProven ability to collaborate across functions (R&D, Privacy, Legal, Infrastructure) and drive cross-team alignment.

PhD in Computer Science, Data Science, Statistics, AI/ML, or a related field.\n\nExperience with Bayesian or causal graph-based approaches to data generation.\n\nExperience identifying low-quality, erroneous, or fraudulent data at scale.\n\nDeep familiarity with generative architectures including transformers, diffusion models, and multi-modal systems.\n\nTrack record of influencing cross-team roadmaps and driving adoption of new tools or infrastructure across organizations.