Senior Machine Learning Engineer – Ranking & Recommendations (Generative AI)

Senior Machine Learning Engineer – Ranking & Recommendations (Generative AI) Department: Engineering Team: Machine Learning Location: San Francisco, United States Type: Full-Time **About the Role** The Shopping Ranking Team mission is enabling eaters to effortlessly make shopping decisions and find what they need. We pursue this mission via an ML-driven algorithmic approach, applying state-of-the-art Machine Learning (ML), Optimization techniques to learn from massive datasets Uber has, and build a scalable and reliable shopping intelligence ranking and recommendation systems. We are actively seeking individuals who excel in problem-solving and critical thinking, are proficient in coding, with proven track records of learning and growth, and have a deep interest in ML model, feature and infrastructure development. Candidates will have the opportunity to work across various lines, from infrastructure development to ML model development, productionalization, offering a diverse and enriching experience. Join us in our pursuit of excellence as we are building the next generation of Generative AI - shopping ranking and recommendation systems. **\-\-\-\- What the Candidate Will Do ----** 1. Design and build Machine Learning models in Ranking and Recommendation domain. 2. Productionize and deploy these models for real-world application. 3. Review code and designs of teammates, providing constructive feedback. 4. Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product. **\-\-\-\- Basic Qualifications ----** 1. Bachelor’s degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 4+ years of full-time engineering experience. 2. 4+ years of ML experience and building ML models 3. Experience working with multiple multi-functional teams(product, science, product ops etc). 4. Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++). 5. Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, , etc. 6. Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorF