Uber's Shopping Ranking Team aims to solve the problem of enabling eaters to effortlessly make shopping decisions and find what they need by applying ML-driven algorithmic approaches to build scalable and reliable shopping intelligence ranking and recommendation systems.
Requirements
- 4+ years of ML experience and building ML models
- 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.
- 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.
Responsibilities
- 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.
- 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.
Other
- 4+ years of full-time engineering experience.
- Experience working with multiple multi-functional teams(product, science, product ops etc).
- Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone.
- Experience owning and delivering a technically challenging, multi-quarter project end to end.
- Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product.
