> Markdown version of [/jobs/ext/4165108-sr-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4165108-sr-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr Machine Learning Engineer

- **Company:** Uber
- **Location:** Sunnyvale, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $202,000.0 - $224,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Neural Networks, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Software Deployment, User-Centered Design, Feature Store, Feature Engineering, Graph Neural Networks, Boosted Trees, Pytorch, Apache Spark, Deep Learning, Scikit Learn, Apache Flink, Data Analytics, Xgboost, Machine Learning Operations, Marketplace
- **Published:** October 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=df83d8869e678570

## About the Role

* 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 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.
*

## Description

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., 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.