**About the Role** The Ads Machine Learning (Ads ML) team at Uber is responsible for providing relevant ad recommendations to the users across the different applications within the Uber ecosystem. We focus on building a deep understanding of both user and merchant behavior to generate accurate ML signals that enhance the Ads auction system. Our goal is to maximize the benefits for both users and merchants within Uber's Ads distribution system. You will directly impact Uber's Ads systems by defining and executing the Ads ML roadmap, with a focus on enabling and accelerating large-scale improvements to our recommendation systems. Developing relevant, robust, and observable ad recommendations is crucial to Uber’s fast growing Ads Business strategy, making this a highly impactful role. **\-\-\-\- What the Candidate Will Do ----** - Design and implement machine learning models and algorithms to optimize ad recommendations and auction mechanisms. - Apply advanced statistical and machine learning techniques to generate insights and improve the effectiveness of ad targeting and delivery. - Define success metrics and develop dashboards to monitor and visualize the performance of ML models in production. - Work closely with cross-functional teams, including Product, Engineering, and Data Science, to translate business requirements into ML solutions. - Mentor and provide technical guidance to junior ML engineers and data scientists. - Stay up-to-date with the latest research and advancements in machine learning, recommendation systems, and ad auction techniques. **\-\-\-\- Basic Qualifications ----** - Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, Data Science, ML, Statistics, or other quantitative fields. - Proven experience with designing and implementing machine learning models in production environments applied to recommendation systems. - Proficiency in using Python for developing ML models and handling large-scale data sets. - Hands-on experience with building batch data pipelines using technologies like Spark or other map-reduce frameworks. **\-\-\-\- Preferred Qualifications ----** - 5 years of industry experience as an ML engineer or equivalent. - Experience with enabling production-scale and maintaining large ML models. - Experience in one or more object-oriented programming languages (e.g. Python, Go, Java, C++) and one ML framework (Pytorch, Tensorflow) - Experience with state-of-the-art deep learning techniques. - Advanced degree (Ph.D. or M.S.) in Data Science, ML, or related disciplines. For New York, NY-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year. 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 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](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.