Uber is hiring: Machine Learning Engineer II in new york

Overview

About The Role: Have you ever ordered a car service on Uber, and when the ride arrives, wondered how it got to you so fast? Have you ever ordered food on UberEats and wondered where the driver was before receiving your order and how long it took to get to the restaurant or if your order was ready when the courier arrived? If so, Uber is for you. In our ML and Science division, we strive to make magic within Uber's marketplace. This requires judgment to make difficult trade-offs, blending algorithms with human resourcefulness, and the ability to build simplicity from complexity. We build systems to peer into the future to craft the most cost-efficient marketplace for matching supply and demand. We are passionate about using innovative economics, machine learning, and scalable distributed software that automates and optimizes every aspect of this intricate dance between participants of the marketplace. We are involved in every stage of the product development cycle and use data to inform product decisions, build models to power our solutions, and develop platform tools used across teams with a primary focus on Mobility and Delivery. We work with millions of earners across the globe to make this magic happen and want you to join us.

About The Team

Earners (drivers and couriers) are an integral part of Uber's multi-sided marketplace. They provide the time and the means to move people and things, enabling the connection between the physical and digital world to make movement happen at the push of a button for everyone, everywhere. Within Uber, Earner plays a critical role in earnings lifecycle, including onboarding, activation, early life cycle, and resurrection. The team shapes the product experience during earners' firsts (e.g., first time interacting on the platform, choosing earning opportunities, going online, receiving incentive offers, completing a trip, or reading the earnings summary). Ensuring a great earner journey at every touch point builds trust and communicates Uber's value proposition.

The team employs a variety of ML/AI techniques—from causal ML meta learners, supervised ML, RL multi-armed bandits, genAI LLMs, transformer modeling on sequential data to deep learning embeddings—to build impactful data products.

What The Candidate Will Do

• Build statistical, optimization, and machine learning models
• Develop innovative new earner incentives to help earners join and stay with our network and optimize Uber's earner incentives spend
• Optimize Uber's background check spend and onboarding funnel
• Design recommendation engines to surface the most relevant earning opportunities and early lifecycle content
• Develop matching algorithms for a driver-to-driver mentorship program
• Model and predict earner behaviors to improve the earner experience throughout the onboarding funnel
• Collaborate with cross-functional teams (product, engineering, operations, marketing) to drive ML system development end-to-end from conceptualization to final product

Basic Qualifications

• PhD, Master, or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or related quantitative field
• 2+ years of industry experience as a Machine Learning Engineer/Research Scientist with a focus on deep learning and probabilistic modeling
• Proficiency in multiple object-oriented programming languages (e.g., Python, Go, Java, C++)
• Experience with Spark, Hive, Kafka, Cassandra or similar big data tools
• Experience building and productionizing end-to-end ML systems
• Experience with exploratory data analysis, statistical modeling, hypothesis testing, and experimental design
• Experience working with cross-functional teams (product, science, product ops, etc.)

Preferred Qualifications

• 3+ years of industry experience in machine learning, including building and deploying ML models
• Publications at recognized ML conferences
• Experience in modern deep learning architectures and probabilistic modeling
• Experience with optimization techniques, including reinforcement learning, Bayesian methods, causal ML meta learners, and genAI LLMs
• Expertise in the design and architecture of ML systems and workflows

For New York, NY; San Francisco, CA; Seattle, WA; and Sunnyvale, CA: the base salary range is USD 171,000 to 190,000 per year. All US locations offer eligibility for Uber's bonus program, potential equity awards, and other compensation types. All full-time employees are eligible for the 401(k) plan and various benefits. More details can be found at

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