Uber is looking for an Engineering Manager to support our Marketplace Investments Modeling team. As the eng lead, you will have ownership of all aspects of growing the team and relevant products. You will manage a high performing team that plays a crucial role in developing and optimizing budget and price level setting for Rides and EATS.
We are broadly part of the Marketplace (PIMS) org, a central pillar to Uber's core technology which includes pricing, incentives/investments, matching, surge, etc. for both mobility and delivery. Investments team builds ML models and data to make incentive spend allocation decisions at Uber. Every week we need to allocate incentive spend across multiple levers to trade-off between revenue, growth, and marketplace health. We are actively working on redesigning Pricing levers and expanding our footprint to Delivery as well.
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What the Candidate Will Do:
Team Building:
- Foster a collaborative, inclusive team culture that values creativity, execution, diversity, and engineering excellence.
- Empower your team to excel in their roles by providing mentorship, guidance, and professional development opportunities.
Technical Leadership:
- Promote the culture of continuous improvement, emphasizing code reviews, design reviews, and knowledge sharing within the team.
- Establish and oversee the vision for the modeling team and create a strong roadmap toward the northstar
- Stay informed about industry trends, emerging technologies, and advancements in machine learning and artificial intelligence.
Multi-functional Collaboration:
- Collaborate with product managers, designers, and operations to develop a vision and roadmap for the team
- Collaborate with data scientists and other engineering teams to understand requirements and integrate different ad products into the data and reporting platform
Operational Excellence:
- Implement thorough OE practices to ensure trust. Robust mechanisms to detect issues and recover from failures
Basic Qualifications:
- A Bachelor's, or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field
- Minimum 5+ years of experience in developing and deploying machine learning models and algorithms in production environments
- Experience in leading teams to build scalable mission-critical systems
Preferred Qualifications:
- Minimum 2 years of management experience
- Excellent communication skills and the ability to collaborate effectively with cross-functional teams
- Experience leading engineering projects from planning through review stages
- Strong leadership and communication skills, with the ability to encourage and empower a diverse team.
- Hands on expertise in career development, mentorship, performance expectations, and conflict resolution
For San Francisco, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 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.
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.
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.