About the job
Uber Eats is the fastest-growing food delivery platform in the world! Our team's work at Uber Eats directly impacts and continues to transform our communities. The **Merchant Intelligence** team is at the heart of this mission, building the foundational systems that help Uber Eats better understand, represent, and categorize every merchant on our platform. As a Machine Learning Engineer on this team, you will focus on improving the quality, consistency, and usability of merchant-related data at a global scale. You will leverage our ML platform to build models that serve critical use cases across Uber, including **Sales and Outreach, Onboarding, Ads and Offers, and Feed Optimization**. This is a unique opportunity to work on large-scale systems where your ML solutions will directly power merchant selection and product experiences for millions of users.
Responsibilities
Innovate and Productionize ML Models: Design and deploy state-of-the-art machine learning models to automate merchant data reconciliation, entity resolution, and data quality improvements.
Build Scalable ML Systems: Architect and maintain end-to-end large-scale ML pipelines that ingest and process complex merchant datasets to power downstream products like Home Feed and Ads.
Feature Engineering: Develop robust merchant embeddings and features that improve the precision of sales outreach and the efficiency of the merchant onboarding process.
Enhance Data Foundations: Improve the ML quality, model serving foundation, and data infrastructure specifically for merchant intelligence.
Cross-Functional Collaboration: Partner closely with Product, Backend Engineering, and Platform teams to translate business needs into scalable ML solutions.
Incremental Impact: Maintain a bias toward shipping incremental improvements that have a clear, measurable impact on user experience and business growth.
Qualifications
Minimum
Experience: PhD or Master in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences and 4 years minimum of industry experience with a strong focus on machine learning and recommendation systems.
Technical Proficiency: Strong coding skills in at least one language such as Python, Java, or Go.
ML Frameworks: Expertise with modern ML frameworks such as PyTorch or TensorFlow.
Systems Design: Experience building and productionizing innovative, end-to-end Machine Learning systems that handle large or complex datasets.
Preferred
Domain Expertise: Experience in simplifying and converting complex business problems (like data consistency and merchant classification) into actionable ML problems.
Large-Scale Systems: Demonstrated ability to develop complex software systems scaling to millions of users with high reliability and monitoring.
Big Data Tools: Familiarity with data processing and streaming tools such as Spark, Hive, Kafka, or Cassandra.
Advanced ML Techniques: Experience with NLP, graph machine learning, or entity resolution is highly advantageous given the team's focus on merchant data.
Mentorship: Proven track record of mentoring junior engineers and driving engineering excellence within a team.
Communication: Strong teamwork and communication skills to effectively collaborate with stakeholders across the organization.