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!

We 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.

Youll 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.

Design and implement synthetic data generation systems across modalities such as: images, video, time series, and text, while using techniques such as GANs, VAEs, diffusion models, Bayesian/Causal methods, and LLM-based synthesis.

Innovate on generation techniques to improve realism and representativeness, particularly for edge cases and underrepresented distributions.

Build and maintain evaluation frameworks to measure synthetic data quality across fidelity, diversity, privacy preservation, and model utility.

Develop pipelines and tools to automate synthetic data generation for large-scale experiments.

Mentor and guide junior ML engineers, conducting code reviews and establishing best practices for synthetic data development.

BS/Masters degree in Computer Science, Engineering, Statistics, or a related quantitative field, alternatively equivalent industry experience may be considered.

5+ years of experience driving the design and development of machine learning pipelines as an ML Engineer.

Hands-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.

Hands-on experience synthesizing time series data at scale.

Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow).

Proficiency in Spark, Ray, or other distributed computing technologies for developing pipelines at scale.

Proficiency in using industry-standard tools and techniques for statistical testing and data experimentation.

Experience with data augmentation across multiple data types (structured, unstructured, and semi-structured).

Strong data exploration and analytical skills, with the ability to assess and characterize diverse data assets.

Proven 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.

Experience with Bayesian or causal graph-based approaches to data generation.

Experience identifying low-quality, erroneous, or fraudulent data at scale.

Deep familiarity with generative architectures including transformers, diffusion models, and multi-modal systems.

Track record of influencing cross-team roadmaps and driving adoption of new tools or infrastructure across organizations.