Job Description

**About the Role** Delivery Marketplace is a central pillar to Uber's delivery products. As the central brain of the company, we are the decision makers that make moving from point A to point B possible for every order that Uber serves, from UberEats to new verticals such as Grocery. We handle all the logic from making the dispatch decisions, predicting how long a delivery might take, and estimating optimal pickup times for orders. We build products that directly impact Uber's top and bottom lines. Staff MLEs lead efforts within the team and broader Delivery Marketplace organization to drive ideation, development and productionization of optimization solutions with real-time and ML model-based signals that solve strategically important problems. Some existing problem spaces that the team works on: + Using statistical/machine learning/forecasting models for demand and supply models + State of the art prediction models for estimating food preparation times, batching quality as well as time spent by couriers at restaurants picking up items. + Develop objective function which balances magical user experience and economics of the business It is a challenging yet rewarding job. You will have a lot of opportunities to work with product managers, data scientists and engineers from other teams. You will guide/mentor a group of MLEs in the end-to-end development cycle from product ideation, model development and productionization at scale. You will be in-charge of solving Uber scale problems with the right techniques like reinforcement learning/deep learning/optimization methods. **What You Will Do** + Lead the design, development, optimization, and productization of machine learning (ML) solutions and systems that are used to solve strategically important or vaguely defined problems. + Build ML solutions to improve Delivery marketplace efficiency while delivering magical user experience + Lead ML engineers, provide technical leadership and vision for the team. **Basic Qualifications** + PhD or equivalent in Computer Science, Engineering, Mathematics or related field **AND** 2-years full-time Software Engineering work experience **OR** 5-years full-time Software Engineering work experience, **WHICH INCLUDES** 3-years total technical software engineering experience in one or more of the following areas: + Programming language (e.g. C, C++, Java, Python, or Go) + Large-scale training using data structures and algorithms + Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) + Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib + Experience with SQL and database systems such as Hive, Kafka, Cassandra, etc + Experience in the development, training, productionization and monitoring of ML solutions at scale. **Preferred Qualifications** + Experience in a technical leadership role and mentoring junior engineers. + Experience in modern deep learning architectures and probabilistic models. + Experience in optimization (RL / Bayes / Bandits) and online learning. + Experience in causal inference/personalization/ranking For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 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 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-

Job Tags

Full time, Work experience placement,

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