Senior ML Engineer, Delivery Zone Optimization

Uber; United States; On-site; USD 202,000 - 224,000; Full time

Benefits offered by this job

Bonus program

Equity

401(k) plan

Benefits

Job summary

Uber is seeking a Senior Machine Learning Engineer for the Deliverability team, shaping core ML systems powering global delivery decisions. You will drive architecture, design, and productionization to optimize merchant coverage and marketplace performance.

Responsibilities include building scalable inference pipelines, developing state-of-the-art models, and leading cross-functional ML initiatives with Product, Data Science, and Operations to raise conversion, throughput, and reliability.

Qualifications

• 5+ years of hands-on professional experience designing, training, and operating production ML models at scale.
• Analytical mindset with ability to derive decisions from complex data distributions.
• Advanced proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Strong foundation in gradient-boosted trees, deep neural networks, and statistical modeling.
• Experience delivering end-to-end ML systems to production with feature pipelines and A/B testing.

Responsibilities

• Architect high-throughput ML solutions for delivery zone optimization.
• Develop and refine models balancing conversion, order volume, and efficiency.
• Build scalable ML inference pipelines, feature stores, and model serving.
• Lead data-driven investigations with online A/B testing and telemetry.
• Partner with PM, Data Science, Ops, and Infra to set ML standards.

Skills

Python

PyTorch

TensorFlow

Scikit-learn

ML at scale

Feature stores

A/B testing

Model monitoring

S2Cell

H3

Tools

H3

S2Cell

Job description

Uber is seeking a Senior Machine Learning Engineer for the Deliverability team, shaping core ML systems powering global delivery decisions. You will drive architecture, design, and productionization to optimize merchant coverage and marketplace performance.

Responsibilities include building scalable inference pipelines, developing state-of-the-art models, and leading cross-functional ML initiatives with Product, Data Science, and Operations to raise conversion, throughput, and reliability.