Engineering Manager II, Rider Personalization

**About the Role** As Manager for the Rider Intelligence team, you will join a team dedicated to using cutting-edge machine learning and AI to create an app experience that feels intuitive and personalized for millions of riders around the world. Your mission will be to ensure that riders always feel understood, with the app effortlessly anticipating their needs-whether they're booking a trip for immediate travel or exploring services that can meet future needs they didn't even know they had. In this role, you will collaborate closely with product managers, data scientists, and cross-functional teams to design scalable systems that enable a seamless, intelligent rider experience. You'll focus on helping users discover the right products and services tailored to their context, unlocking new possibilities and ensuring Uber is there for them at every step of their journey. This will be powered by building ML models and infrastructure staying at the cutting edge of ML/AI research in the industry. This is an exciting opportunity to be part of a broader Rider ML team with a far-reaching mission to personalize many aspects of the app and make Uber feel like a personal assistant that's always one step ahead. **What You'll Do** - Define and execute the Rider ML product and engineering roadmap and strategy, focusing on personalization and discovery experiences that enhance the rider journey. - Direct the design and deployment of real-time machine learning services and APIs that are critical to business success, supporting diverse platform goals across Uber’s business lines. These goals include enhancing engagement, boosting transactions and bookings, improving ranking and discovery efficiency, especially in cold-start scenarios. - Partner effectively with cross-functional teams to gather real-time, high-quality data essential for content ranking and personalization. These collaborations span engineering partners, product managers, data scientists, designers, and operations teams. - Collaborate with key stakeholders to define key results (KRs) and align the team’s execution plans accordingly. Build scalable processes to support partner teams’ integration with the ranking platform. - Coordinate with technical leads to develop robust on-call protocols, along with alerting and monitoring systems, for both the machine learning models and serving infrastructure. - Stay informed on the latest AI/ML research and industry advancements, identify promising methodologies, and motivate the team to evaluate and integrate relevant innovations into production. - Hire, develop, and retain top-tier machine learning professionals while nurturing a culture centered on innovation, teamwork, and technical mastery. **Basic Qualifications** - MS or equivalent experience in Computer Science, Engineering, Mathematics or related field - 8+ years of industry experience as an Applied Scientists/Machine Learning Engineer with 3+ years of experience leading a team as a manager. - Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design. - Experience working with cross-functional teams(product, science, product ops etc). **Preferred Qualifications** - Ph.D. in Computer Science, Machine Learning, or a closely related discipline. - 8+ years of industry experience designing, deploying, and operating large-scale production machine learning systems. - Background in search, recommendation systems, ranking/retrieval, or representation learning is highly desirable. - 3+ years of leadership experience managing engineering teams, either as a Tech Lead Manager (TLM) or Engineering Manager (EM), with strong cross-functional collaboration and communication skills. - Practical experience building production-grade recommender systems or ML systems such as search, speech/audio, or image recognition, with proficiency in deep learning frameworks like PyTorch, Keras, or TensorFlow. - Demonstrated success leading cross-functional initiatives with tight deadlines. Excellent at communication, alignment-building, and influence across complex organizational structures. - Experience at coaching/mentoring engineers to elevate performance and keep top talent motivated and engaged. 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 Seattle, WA-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 [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits). Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together. Uber is proud to be an Equal Opportunity 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.