Sr Machine Learning Engineer

Senior Machine Learning Engineer role on Uber’s Deliverability team focused on end-to-end ML design and productionization for the global delivery marketplace.

Responsibilities

• Architect and translate complex marketplace challenges into high-throughput, large-scale machine learning solutions for delivery zone optimization.
• Develop, evaluate, and iteratively improve state-of-the-art ML models that dynamically optimize merchant delivery coverage while balancing conversion, order volume, and fulfillment efficiency.
• Build, deploy, and run scalable ML inference pipelines, feature stores, model serving infrastructure, and automated retraining workflows for high-throughput global delivery evaluations.
• Conduct data-driven investigations into marketplace inefficiencies and use telemetry to improve core business metrics.
• Execute online A/B testing to validate improvements in production.
• Collaborate with Product Management, Data Science, Operations, and Core Infrastructure 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.
• Strong analytical mindset with a focus on extracting insights from complex data distributions to support technical decisions and project alignment.
• Advanced proficiency in Python and mainstream ML frameworks: PyTorch, TensorFlow, and Scikit-learn.
• Solid theoretical grounding in classic and modern ML algorithms, including (but not limited to) gradient-boosted trees (XGBoost, LightGBM), deep neural networks, and statistical modeling.
• Demonstrated ability to take ML systems end-to-end from problem formulation and offline validation through large-scale production deployment.
• Practical experience creating automated feature engineering pipelines; using feature store technologies; operating model monitoring platforms; and running 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.
• Proven ability to drive cross-functional technical initiatives, mentor junior engineers, and deliver complex multi-phase engineering projects.
• Familiarity with distributed data processing systems including Spark, Ray, and Flink, plus foundational concepts in two-sided marketplace optimization.

Compensation

• San Francisco, CA: base salary range USD $202,000 - $224,000 per year.
• Sunnyvale, CA: base salary range USD $202,000 - $224,000 per year.
• All US locations: eligible to participate in Uber’s bonus program; 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.

About the Team

• Deliverability team sits at the core of Uber’s delivery marketplace within Merchant Optimization.
• Systems serve as a critical gating layer for every Uber Eats transaction, answering whether a merchant can deliver to an eater right now.
• Optimizes merchant delivery zones to maximize platform selection while balancing reliability, timeliness, and profitability.
• Deliverability decisions impact selection breadth and business metrics across downstream platforms including Search, Feed, and Ads.

Work Location / Remote Work Requirement

• San Francisco, CA onsite.
• Offices remain key for collaboration and culture; unless approved for full remote work, employees must spend at least 50% of their time in-office.
• Some roles (for example, greenlight hubs) may require full-time in-office presence.