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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.
Job Responsibility:
Design and build Machine Learning models in Ranking and Recommendation domain
Productionize and deploy these models for real-world application
Review code and designs of teammates, providing constructive feedback
Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product
Requirements:
Bachelor’s degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 4+ years of full-time engineering experience
4+ years of ML experience and building ML models
Experience working with multiple multi-functional teams(product, science, product ops etc)
Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++)
Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, etc
Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorFlow, Ray, etc
Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone
Nice to have:
Experience with building ranking and recommendation systems in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments
Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact
Experience with design and architecture of ML systems and workflows
Experience owning and delivering a technically challenging, multi-quarter project end to end
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan
Eligible for various benefits (details at provided link)