The Machine Learning Engineer role at Uber Eats focuses on enhancing the quality and usability of merchant-related data on a global scale. The Merchant Intelligence team is responsible for building systems that improve merchant understanding and categorization. This position involves leveraging machine learning to support various critical use cases across the company. Candidates will have the opportunity to work on large-scale systems that impact millions of users.

• PhD or Master in relevant fields such as Computer Science, Electrical Engineering, Mathematics, or Statistics.
• A minimum of 4 years of industry experience with a strong focus on machine learning and recommendation systems.
• Strong coding skills in at least one programming language such as Python, Java, or Go.
• Expertise with modern machine learning frameworks such as PyTorch or TensorFlow.
• Experience building and productionizing end-to-end machine learning systems that handle large or complex datasets.

• Design and deploy state-of-the-art machine learning models to automate merchant data reconciliation and improve data quality.
• Architect and maintain end-to-end large-scale machine learning pipelines for processing complex merchant datasets.
• Develop robust merchant embeddings and features to enhance sales outreach and merchant onboarding efficiency.
• Improve the machine learning quality and data infrastructure specifically for merchant intelligence.
• Collaborate closely with Product, Backend Engineering, and Platform teams to translate business needs into scalable machine learning solutions.
• Maintain a focus on delivering incremental improvements that have measurable impacts on user experience and business growth.

• Employees at Uber are often offered comprehensive health, life, disability, and mental wellness benefits, along with wellbeing stipends, travel medical coverage, and monthly Uber credits for Rides and Eats. Employees also get generous paid parental leave, flexible time off, and family-planning support so they can care for themselves and their families at every stage.