UB
UberSr Machine Learning Engineer
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Before you apply
SalaryNo pay range posted
LocationSunnyvale, CA, United States
Work modehybrid
Typefull-time
SenioritySenior
Experience5+ yrs
EquityIncluded
Company size10,000+ people
First seenOct 22, 2026 · 1w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
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
PhD candidate with 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
Skills
Python
PyTorch
TensorFlow
Scikit-learn
XGBoost
LightGBM
H3
S2 Cell
Spark
Ray
Flink
Benefits
Bonuses
Equity
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.
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.
Benefits
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.
Legal
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.