About the Role The Investment Modeling Team at Uber is at the forefront of driving the company's global incentive and pricing strategies across all pricing and incentive mechanisms and cities worldwide! Encompassing both Mobility and Delivery businesses, we help Uber hit more aggressive growth and profitability targets while maintaining the overall health of the marketplace. We pursue this objective via an ML-driven algorithmic approach, applying state-of-the-art Machine Learning (ML) and Optimization techniques to learn from massive datasets Uber has, estimate the composite marketplace pricing and incentive impact under various conditions, and identify the optimal investment strategy!
To support and facilitate this work, we have also developed our in-house ML and optimization infrastructure, including data ETL, feature store, dev & viz tooling, model training, serving, storage and backtest solutions. We extensively use the latest technologies and libraries, such as HDFS, Spark, Ray, PyTorch, Horovod, Modin, etc, in our 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 prior experience in ML model, feature, and infrastructure development.
Join us in our pursuit of excellence as we continue to shape the future of Uber's global incentive and pricing strategies through innovative engineering and model-driven insights.
What the Candidate Will Do Design and build Machine Learning models with optimization engines. 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. Basic Qualifications Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 4+ years of full-time engineering experience. 2+ 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. Preferred Qualifications Experience with the design and architecture of ML systems and workflows. Experience with building algorithmic solutions 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 optimizing Spark queries for better CPU and memory efficiency. Experience owning and delivering a technically challenging, multi-quarter project end to end.
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