## **About the role:** This is a key role as a thought leader and key contributor to Machine Learning efforts across several key domains in Marketplace - Job-Driver Matching system, Driver offer pricing, and Driver Surge pricing. The ML models in these domains vary from causal ML models, reinforcement learning models, and forecast models. Some of the challenges in these domains is dealing with data sparsity and delay in realizing the impact of actions given the physical nature of Uber business, network effects given the drivers are a limited supply that are shared across riders, long term behavioral changes in driver community and geo differences in driver values and Uber business - all of these considerations make this problem space a challenging and open problem in ML field. The impact of this role is extremely high given the impact of the marketplace levers it supports. ## **About the Team:** The org includes Driver offer pricing, Matching, and Driver surge teams within the Uber Marketplace organization. The team owns systems that make optimum decisions on driver pricing and job-driver matching, working cross functionally with various organizations at Uber across Earner and Rider teams, Operations, and Platforms. ## **Minimum qualifications:** - PhD or equivalent in Computer Science, Engineering, Mathematics or related field **AND** 6-years full-time Software Engineering work experience **OR** 10-years full-time Software Engineering work experience, **WHICH INCLUDES** 6-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 - _Note the 6-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these).  The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated._ ## **Technical skills:** ## Required: - Deep Learning - Scalable ML architecture - Feature management Preferred: - Causal ML - Reinforcement learning - Contextual bandit models - Personalization and ranking experience For San Francisco, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year. 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](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://forms.gle/aDWTk9k6xtMU25Y5A). Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.