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# Sr Machine Learning Engineer

- **Company:** Uber
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $202,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Big Data, Software Debugging, Distributed Systems, Python (Programming Language), Machine Learning, Software Engineering, Information Technology, Optimization Algorithms, Apache Flink, Apache Kafka, Marketplace, Stream Processing, Golang, Programming Languages
- **Published:** October 10, 2026
- **Apply:** https://dejobs.org/x/x/D20406D88AE24966BE656816FF470387/job/

## About the Role

1. 5+ years of professional software engineering experience in ML, statistics, or optimization techniques, distributed systems, or large-scale data processing.
 2. Proficiency in one or more programming languages (e.g., Go, Java, Python).
 3. Experience designing and operating high-throughput, low-latency services, and translating customer or business problems into system design.
 4. Bachelor's Degree (or equivalent experience) in Computer Science, Engineering, or a related technical field., 1. Experience with marketplace, pricing, dispatch, forecasting, or other real-time decision systems.
 2. Familiarity with real-time data streaming (e.g., Kafka, Flink) or time-series forecasting pipelines.
 3. Demonstrated ability to exercise sound judgment on engineering trade-offs where algorithm behavior has direct business impact.

## Description

Engineering at Uber means building for real-world impact under real-world constraints. As a L5 MLE on the Pricing & Incentives team team, you will build innovative products and scalable systems that power our global marketplace. Our systems produce real-time decision signals and sophisticated algorithms that other critical systems depend on every second. The problems here are complex, the systems are massive, and the stakes are immediate.

What you'll do

 1. Requires advanced ML expertise to design machine learning models to jointly optimize demand levers (TDF, TSF, HVAs, EUP). This role is necessary to improve marketplace efficiency by modeling the coordination across pricing and incentive levers.
 2. Own the software lifecycle end-to-end: from design docs through production rollout, debugging live issues, and managing incidents.
 3. Collaborate across disciplines - Product, Applied Science, and Ops - to translate ambiguous marketplace problems into robust system designs.