**About the Teams:** **Coordinated Structural Pricing:** The coordinated structural pricing and simulator team works on setting rider and driver prices to achieve marketplace balance. We maintain many offline models to simulate and optimize marketplace price levels at a coarse granularity to achieve growth in trips and GB while maintaining marketplace reliability and VC neutrality. We are actively working on a new pricing and simulation tech called Pricer-Planner and exploring many different avenues for simulation, both ML-driven and structural economics-driven. **Marketplace Intelligence:** The Uber Marketplace team is at the core of Uber's business, and Marketplace Intelligence aims to mitigate negative customer experience and perception through intelligence. The mission of the team is to foster growth and reduce user churn at Uber by pushing the frontiers of machine learning, data science and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry. This role will drive high-impact projects to optimize consumer marketplace experiences at Uber using optimization, machine learning, and causal inference. **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 or PhD with 2+ years of full-time engineering experience + 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++). **Preferred Qualifications:** + 3+ years of ML/economics experience and building ML/economic models + 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. + Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorFlow, JAX, Ray, etc. + Experience owning and delivering a technically challenging, multi-quarter project end to end. + Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, , etc. For San Francisco, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year. For Seattle, WA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits. Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form- https://docs.google.com/forms/d/e/1FAIpQLSdb_Y9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform