Requirements

Masters or PhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or other related quantitative fields or related field
7 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).

Nice to Haves

8+ 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.

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.

Offices continue to be central to collaboration and Uber's cultural identity.
Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.