About the Role
The Consumer Incentives team is responsible for the profitability and growth trajectory of Uber's business across various verticals, including food and grocery. Our objective is to enhance the customer experience by making it more pleasant and affordable. The team addresses complex challenges in machine learning, optimization, and distributed systems to power products that serve hundreds of millions of individuals globally.
---- What You Will Do ----
In this role, you will provide ML technical leadership, help identify gaps/opportunities, and influence the direction of technical solutions to enhance incentive efficiency, while optimizing user experience across various verticals, including food and grocery.
Key responsibilities include:
- Identifying strategic technical investments to push the efficiency frontier and boost business growth.
- Leading teams to design and implement ML/optimization solutions to meet ambitious business goals.
- Managing end-to-end project execution, from scoping and offline evaluation to experimentation, production, and post-launch operation.
- Collaborating with cross-functional teams, including product, operations, and science partners.
---- Basic Qualifications ----
- Master (or equivalent in Computer Science, Engineering, Mathematics or related field) with 6+ years of full-time ML engineering experience
- Expertise in deep learning and optimization algorithms.
- Experience with ML frameworks such as PyTorch and TensorFlow.
- Experience building and productionizing innovative end-to-end Machine Learning systems.
- Proficiency in one or more coding languages such as Python, Java, Go, or C++.
- Strong communication skills and can work effectively with cross-functional partners.
---- Preferred Qualifications ----
- PhD in relevant fields (CS, EE, Math, Stats, etc.) with a focus on Machine Learning and 4+ years of experience in ML role with an emphasis on data and experiment driven model development.
- Experience in serving and monitoring online training systems such as real time recommendation systems.
- Experience designing and implementing novel metrics for performance evaluation.
- Proven track record in conducting experiments and tracking models in high-complexity environments.
For New York, NY-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 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. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/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.
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.