Machine Learning Engineer II - Payment Fraud

About us:

At , data drives our decisions. Technology is at our core, and innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make.

Through our products, partners and people, we make it easier for everyone to experience the world.

Payment Fraud Engineering and Machine Learning teams are dedicated to safeguarding ’s platform by building and deploying end-to-end Machine Learning solutions. We focus on developing robust models for detection, prevention, and proactive intervention against various payment space threats, leveraging data, classical and advanced Deep Learning techniques to create a secure environment for our users. Our work directly contributes to maintaining user trust and platform integrity.

As a Machine Learning Engineer at , your work will focus on training and deploying machine learning models (Classification, Deep Learning, Optimization, Natural Language Processing, Active Learning) using the most advanced technologies and models. You will be responsible for identifying and using the most appropriate data sources and modeling techniques to solve complex problems and drive measurable business value.

You will also help in building new products and solutions by driving a research agenda and development plan from ideation to prototyping to full productionisation in the team, and using opportunities to contribute to a vibrant department’s ML community.

• Develop production-grade ML systems, from models to features and pipelines, accounting for reliability, scalability, real-time requirements, monitoring and retraining.
• Build readable and reusable code, applying code quality best practices and using standard libraries. Choose the right technology or coding methodology as well as refactor and simplify code when necessary.
• Take full ownership of your services end to end by actively monitoring the systems health, performance and business impact.
• Be responsible for business related data governance processes, the technical implementation and maintenance of data processing services and storage systems, and the implementation and maintenance of ML governance processes.
• Evaluate possible architecture solutions taking into account the business and technology requirements.
• Set the relevant service level objectives SLOs and act accordingly when they are not met.
• Build readable and reusable code, using the right technologies and coding methodologies applying knowledge of business area tools and product needs.
• Continuously evolve your craft by keeping up to date with the latest developments in ML/AI and related technologies and upskilling on these, as needed.
• Contribute to the internal ML/AI community by sharing your knowledge and participating in our internal ML programs.
• Coach others through evidence and clear communication, explaining advanced technical concepts in simpler terms.
• Maintain a highly cross-disciplinary perspective, solving issues by applying approaches and methods from across a variety of disciplines and related fields.
• Achieve mutually agreeable solutions by staying adaptable, communicating ideas in clear coherent language and practising active listening.

• Bachelor’s or master’s degree in Computer Science, Engineering, Statistics, or a related field.
• Minimum of 4 years of experience as a Machine Learning Engineer or a similar role, with a consistent record of successfully delivering ML solutions.
• Strong programming skills in languages such as Python and Java.
• Experience with cloud frameworks like GCP/AWS for training, evaluation and serving ML models using Tensor Flow, PyTorch, or scikit-learn.
• Experience with big data processing frameworks such, Big Query, PySpark, Snowflake or similar frameworks.
• Deep understanding of machine learning algorithms, statistical models, and data structures.
• Experience in deploying and inference for large-scale machine learning models - an advantage.
• Proficiency in…