Machine Learning Engineer II

Machine Learning Engineer II Department: Engineering Team: Machine Learning Location: New York, United States Type: Full-Time **About the Role** The UberEats Feed is the front door to our service. It serves an important role for both users and merchants. For our users, the Feed helps them find a great restaurant or grocery store for their needs. It also serves as an important gateway for them to explore the breadth and depth of UberEats’s selection. For merchants, it is the main surface for which they get in front of potential customers to showcase their products. As a Machine Learning Engineer in this role, you will be able to work on various open-ended, challenging, impactful problems. **What You'll do** - Innovate and productionize start-of-the-art recommendation models, and customize for Uber’s use cases. - Design and build the end-to-end large-scale ML systems to power the HomeFeed Recommendation. - Improve the Feed Model ML Quality, Model Serving foundation and the Data foundation. - Collaborate with cross-functional and cross-team stakeholders. **Basic Qualifications** - PhD in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences or 3 years minimum of industry experience with a strong focus on machine learning and recommendation systems. - Expertise in deep learning, recommendation systems, or optimization algorithms. - Experience with ML frameworks such as PyTorch and TensorFlow. - Experience building and productionizing innovative end-to-end Machine Learning systems. - Proficiency in one or more coding languages such as Python, Java, Go, or C++. **Preferred Qualifications** - Publications at industry recognized ML conferences. - Experience in simplifying/converting business problems into ML problems. - Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability. - Experience with any of the following: Spark, Hive, Kafka, Cassandra. - Strong communication skills and can work effectively with cross-functional partners. For New York, NY-based roles: The base s