Sr Machine Learning Engineer

The Senior Machine Learning Engineer will join Uber’s Deliverability team within Merchant Optimization, helping design and productionize machine learning systems that support global delivery marketplace decisions. This role focuses on end-to-end delivery zone optimization, scalable model deployment, and experimentation driven by telemetry.

Role Overview

As a member of Deliverability, you will address the real-time marketplace question of merchant deliverability for Uber Eats transactions. You will contribute to optimizing merchant delivery zones to improve platform selection while balancing reliability, timeliness, and profitability. The outcomes from deliverability services influence downstream business metrics across Search, Feed, and Ads.

Responsibilities

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

Requirements

• 5+ years of hands-on experience designing, training, and operating production machine learning models at scale.
• Strong analytical mindset with an interest in extracting insights from complex data distributions to guide technical decisions and secure project alignment.
• Advanced proficiency in Python and mainstream machine learning frameworks, such as 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 ability to take complex ML systems from problem formulation and offline validation through large-scale production deployment.
• Practical experience building automated feature engineering pipelines, working with feature store technologies, using 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 lead cross-functional technical initiatives, mentor junior engineers, and deliver complex multi-phase engineering projects.
• Familiarity with distributed data processing systems such as Spark, Ray, and Flink, along with foundational concepts in two-sided marketplace optimization.

Compensation

Base salary range (San Francisco, CA and Sunnyvale, CA): USD $202,000 per year to USD $224,000 per year.

Bonus and equity: For all US locations, you will be eligible 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 (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 / Remote Work Requirement

Offices remain key to collaboration and Uber’s culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence.