Sr Machine Learning Engineer

On Uber’s Deliverability team, you’ll help build machine learning systems that support real-time decisions across the delivery marketplace. The work connects end-to-end model design, productionization, and experimentation to improve merchant coverage and marketplace reliability, timeliness, and profitability.

As a Senior Machine Learning Engineer, you will focus on delivery zone optimization and the infrastructure that powers scalable ML inference, retraining, and telemetry-driven experimentation. The Deliverability services act as a gating layer for Uber Eats transactions, determining whether a merchant can deliver to an eater right now, and the resulting choices influence selection breadth and downstream business metrics across Search, Feed, and Ads.

Responsibilities

• Architect and translate complex marketplace challenges into high-throughput, large-scale ML solutions for delivery zone optimization.
• Develop, evaluate, and continuously refine state-of-the-art machine learning models that dynamically optimize merchant delivery coverage while balancing conversion, order volume, and fulfillment efficiency.
• Engineer, deploy, and operate scalable ML inference pipelines, feature stores, model serving infrastructure, and automated retraining workflows for high-throughput global delivery evaluations.
• Lead data-driven investigations to diagnose marketplace inefficiencies and use online A/B testing and telemetry to improve core business metrics.
• Collaborate closely with Product Management, Data Science, Operations, and Core Infrastructure teams to align technical roadmaps and define cross-platform ML standards.

Requirements

• 5+ years of hands-on professional experience designing, training, and operating production ML models at scale.
• Proactive, strong analytical mindset with a focus on extracting insights from complex data distributions to support technical decisions and project buy-in.
• Advanced proficiency in Python and mainstream machine learning frameworks, including PyTorch, TensorFlow, and Scikit-learn.
• Solid theoretical foundation in classic and modern ML algorithms, including gradient-boosted trees (XGBoost, LightGBM), deep neural networks, and statistical modeling.
• Demonstrated experience taking complex ML systems end-to-end from problem formulation and offline validation through large-scale production deployment.
• Practical experience building automated feature engineering pipelines, using feature store technologies, model monitoring platforms, and executing controlled online A/B testing in production.

Technologies

• Python
• PyTorch
• TensorFlow
• Scikit-learn
• XGBoost
• LightGBM
• Feature store technologies

Preferred Qualifications

• PhD candidate with 3+ years of industry experience.
• Experience with geospatial libraries such as H3 and S2 Cell.
• Experience with Graph Neural Networks (GNNs) and/or deep learning recommendation systems.
• Ability to drive cross-functional technical initiatives, mentor junior engineers, and deliver complex multi-phase engineering projects.
• Familiarity with distributed data processing systems like Spark, Ray, and Flink, and foundational concepts in two-sided marketplace optimization.

About the Team

The Deliverability team sits at the core of Uber’s delivery marketplace as part of Merchant Optimization. Serving as the critical gating layer for every Uber Eats transaction, the systems answer the real-time question of whether a merchant can deliver to an eater right now. The team optimizes merchant delivery zones to maximize platform selection while balancing marketplace reliability, timeliness, and profitability, with deliverability decisions shaping selection breadth and business metrics across downstream platforms such as Search, Feed, and Ads.

Compensation

• Base salary (San Francisco, CA): USD 202,000 - 224,000 per year.
• Base salary (Sunnyvale, CA): USD 202,000 - 224,000 per year.
• Bonus and equity: For all US locations, eligibility to participate in Uber’s bonus program, and may be offered an equity award and other types of comp.

Benefits

• Eligible to participate in Uber’s bonus program for all US locations.
• May be offered an equity award and other types of comp.
• 401(k) plan eligibility for all full-time employees.
• Eligible for various benefits.

Work Location

• Location: San Francisco, CA (onsite).
• Offices remain key to collaboration. Unless approved for full remote work, employees must spend at least 50% of their time in-office.
• Some roles, including those at greenlight hubs, require full-time in-office presence.

Minimum experience: 5 years.