Machine Learning Engineer II

Have you ever ordered a car service or food delivery on Uber and wondered how everything coordinates so seamlessly behind the scenes? In our ML and Science division, we strive to make magic within Uber’s marketplace by blending algorithms with human resourcefulness to build simplicity from complexity. We peer into the future to craft the most cost-efficient marketplace for matching supply and demand using innovative economics, machine learning, and scalable distributed software.

Our team focuses heavily on Earners—the drivers and couriers who provide the time and means to move people and things, enabling the connection between the physical and digital world. Within this team, you will shape the product experience for earners during their many critical first interactions with the platform, building trust and ensuring a great journey from onboarding and activation through their early lifecycle.

As a Machine Learning Engineer II, what you will do:

• Build statistical, optimization, and machine learning models to power innovative solutions across mobility and delivery.
• Develop innovative new earner incentives and optimize incentive spend, alongside optimizing background check spend and onboarding funnels.
• Design recommendation engines to suggest relevant earning opportunities and early lifecycle content.
• Develop matching algorithms for driver-to-driver mentorship programs and model earner behaviors to improve the overall onboarding experience.
• Apply a variety of ML and AI techniques, including causal ML meta-learners, supervised ML, reinforcement learning multi-armed bandits, generative AI LLMs, transformer modeling on sequential data, and deep learning embeddings.
• Work closely with multi-functional leads and cross-functional teams such as product, engineering, operations, and marketing to drive technical vision and end-to-end ML system development.

What the candidate will need (Basic Qualifications):

• A PhD, Master’s degree, or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or a related quantitative field.
• A minimum of 2 years of industry experience as a Machine Learning Engineer or Research Scientist with a strong focus on deep learning and probabilistic modeling.
• Proficiency in multiple object-oriented programming languages such as Python, Go, Java, or C++.
• Experience with technologies such as Spark, Hive, Kafka, or Cassandra.
• Demonstrated 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 collaboratively with cross-functional teams.

Preferred Qualifications:

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

For roles based in New York, NY; San Francisco, CA; Seattle, WA; or Sunnyvale, CA, the base salary range is USD $171,000 per year to USD $190,000 per year. For all US locations, employees are eligible to participate in Uber’s bonus program, may be offered an equity award and other types of compensation, and can participate in a 401(k) plan along with various other benefits.

Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office to support collaboration and our cultural identity. Uber is proud to be an Equal Opportunity employer, committed to fostering an inclusive and diverse workforce.

• Job Category: Engineering & IT