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Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About the team and the role:

The ML engineers will work closely with Applied Researchers, Policies and other partners to identify the business requirements, using the in-house or open-source tools and framework to provide the end to end solution for model training, model deployment and model online or offline inference on distributed clusters. As a member of the decision science team, you will research and develop new methodologies and techniques to improve the overall efficiency of risk & compliance management.

What you will accomplish:Design, implement and optimize scalable ML data pipeline to train various models aimed at identifying suspicious activities on global e-commerce platforms.Partner closely with applied researchers to fetch, clean up, generate batched or real time features and manage large datasets for reliable model training and evaluation.Work continuously with collaborators to provide end to end online or offline Inferencing solutions to effectively catch fraudulence, mitigate buyer and seller risk, align with constantly changing or developing regulations as a global payment entity.Develop in house scheduling tools to submit data related tasks to different components like Hadoop, training framework, report tools and customer service systems.Implement and optimize transformer data loading, processing and Inferencing on distributed GPU clusters to improve GPU usage.Act as the technical owner to connect with different partners to collect requirements, clarify the ambiguity, identify the long-term, scalable and reusable solutions.Continuously monitor robot data collection process and evaluate data quality.Mine and analyze massive amounts of unique internal and external data to gain deep business knowledge and insight on customer activity and usage behaviors and their relationships with fraud, credit risks, anti-money laundering, and other types of behaviors.Good to have MLops related experience like online model deployment.What you will bring:MS in computer science or related or equivalent experience.3+ years of industry experience applying machine learning methods.End-to-End hands-on experience with building data processing pipelines, large scale ML systems and big data technologies such as Hadoop/Spark/Scala.Practical knowledge of system design with scalability, performance, security, and maintainability in mind while making informed decisions based on the problem domain and user needs.Expertise in at least one object-orienting or scripting language, python, Java, Scala etc.Excellent problem-solving skills and the ability to work optimally across functional teams.In-depth knowledge of Machine Learning / Deep Learning / NLP.Good to have experience implementing LLM or agentic system.Good to have experience developing tools based on Airflow.

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