What You’ll Do

Build statistical, optimization, and machine learning models
Develop innovative new earner incentives that earners for choosing our network and optimizing Uber’s new earner incentives spend
Optimize Uber’s background check spend and onboarding funnel
Design recommendation engines to recommend the most relevant earning opportunities and early lifecycle content
Develop matching algorithms for driver to driver mentorship program
Model and predict earner behaviors to improve earner experience throughout the onboarding funnel
The team employs a variety of ML/AI techniques, spanning from causal ML meta learners, supervised ML, RL multi-armed bandits, genAI LLM to deep learning embeddings to build impactful data products.
Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
Collaborate with cross-functional teams such as product, engineering, operations, and marketing to drive ML system development end-to-end from conceptualization to final product.
Basic Qualifications

PhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or other related quantitative fields or related field
4 years minimum of industry experience as a Machine Learning Engineer/Research Scientist with a strong focus on deep learning and probabilistic modeling.
Proficiency in multiple object-oriented programming languages (e.g. Python, Go, Java, C++).
Experience with any of the following: Spark, Hive, Kafka, Cassandra.
Experience building and productionizing innovative end-to-end Machine Learning systems.
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

5+ years of industry experience in machine learning, including building and deploying ML models.
Publications at industry recognized ML conferences.
Experience in modern deep learning architectures and probabilistic modeling.
Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM.
Expertise in the design and architecture of ML systems and workflows.
For New York, NY-based roles: The base salary range for this role is USD$198,000 per year – USD$220,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year – USD$220,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$198,000 per year – USD$220,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$198,000 per year – USD$220,000 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.