Sr Machine Learning Engineer

Uber Technologies Inc.; Sunnyvale (CA); On-site; USD 202,000 - 224,000; Full time

Benefits offered by this job

Bonus program

Equity awards

401(k) plan

Benefits package

In-office collaboration

Job summary

Uber Technologies Inc. is seeking a Senior Machine Learning Engineer for the Deliverability team to drive the technical vision and end-to-end design of core ML systems powering global delivery decisions.

You will architect solutions for delivery zone optimization, train models balancing conversion and fulfillment, and deploy scalable inference pipelines. The role requires 5+ years in production ML, strong Python skills, and expertise in PyTorch, TensorFlow, and Scikit-learn.

Qualifications

• 5+ years of hands-on professional experience designing, training, and operating production ML models at scale.
• Proactive and strong analytical mindset with ability to uncover insights from complex data distributions to drive decisions.
• Advanced proficiency in Python and mainstream ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
• Solid theoretical foundation in classic and modern ML algorithms, including gradient-boosted trees, deep neural networks, and statistical modeling.
• Proven record of end-to-end ML systems deployment from problem framing to production.

Responsibilities

• Architect and translate marketplace challenges into high-throughput ML solutions for delivery zone optimization.
• Design, train, and refine production ML models that optimize merchant delivery coverage.
• Engineer and operate scalable ML inference pipelines, feature stores, and model serving infra.
• Lead data-driven investigations, online A/B testing, and telemetry-driven improvements.
• Collaborate with Product, Data Science, Operations, and Core Infrastructure teams to align roadmaps.

Skills

Python

Analytical mindset

Strong ML fundamentals

Education

PhD candidate

Tools

PyTorch

TensorFlow

Scikit-learn

XGBoost

LightGBM

Job description

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, our systems answer the fundamental real-time question: "Can this merchant deliver to this eater right now?"

We optimize merchant delivery zones to maximize platform selection while balancing marketplace reliability, timeliness, and profitability. The decision made by the deliverability services directly shape selection breadth and business metrics across key downstream platforms, including Search, Feed, and Ads.

Role & Responsibilities

As a Senior Machine Learning Engineer on the Deliverability team, you will drive the technical vision, end-to-end design, and productionization of core ML systems powering global delivery marketplace decisions. Specifically, you will:

• Product & Domain ML Architecture:Architect and translate complex marketplace challenges into high-throughput, large scale ML solutions for delivery zone optimization.
• Design & Train Core Models:Develop, evaluate, and continuously refine state-of-the-art machine learning models that dynamically optimize merchant delivery coverage, balancing conversion, order volume, and fulfillment efficiency.
• Production Pipelines & ML Infrastructure:Engineer, deploy, and operate scalable ML inference pipelines, feature stores, model serving infrastructure, and automated retraining workflows handling high-throughput global delivery evaluations.
• Marketplace Optimization & Experimentation:Lead data-driven investigations to diagnose marketplace inefficiencies, execute online A/B testing and leverage telemetry to drive core business metrics.
• Cross-Functional Technical Leadership:Partner closely with Product Management, Data Science, Operations, and Core Infrastructure teams to align technical roadmaps and define cross-platform ML standards.

Basic Qualifications

• 5+ years of hands-on professional experience designing, training, and operating production ML models at scale.
• Proactive and strong analytical mindset with a passion for uncovering insights from complex data distributions to drive technical decision-making and project buy-in.
• Advanced proficiency in Python and mainstream machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
• Solid theoretical foundation in classic and modern ML algorithms, including and not limited to gradient-boosted trees (XGBoost, LightGBM), deep neural networks, and statistical modeling.
• Proven record of taking complex ML systems end-to-end from initial problem formulation and offline validation to large-scale production deployment.
• Practical experience developing automated feature engineering pipelines, using feature store technologies, model monitoring platforms, and executing controlled online A/B testing in production.

Preferred Qualifications

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

For San Francisco, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.

Ready to Ride?

This isn't the kind of place where you follow a playbook - it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves - we'd love to hear from you.

You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.

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. Ask your Recruiter for details about this role's requirements.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.