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# Sr Software Engineer - Engineer

- **Company:** Uber
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $202,000.0 - $224,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), C++ (Programming Language), Code Review, Data Centers, Software Debugging, Distributed Computing Environment, Distributed Systems, Fault Tolerance, Job Scheduling, Python (Programming Language), Machine Learning, Open Source Technology, Azure Machine Learning, Data Driven Tests, Workflow Management Systems, Build Management, Kubernetes, Information Technology, Machine Learning Operations, Golang, Programming Languages
- **Published:** November 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c570a9847ce2e59b

## About the Role

* 6+ years of professional experience building scalable, fault-tolerant products or platforms, with a track record of solving important problems.
 * Proficiency in one or more programming languages (e.g., Go, C/C++, Java, Python, or JavaScript).
 * Deep understanding of Kubernetes and Ray, beyond using them as a user. This means knowing how they work internally and how to extend, operate, and debug them at scale.
 * Experience with compute infrastructure, such as cluster management, job scheduling, or resource orchestration.
 * Proven experience delivering large-scale engineering projects through the entire lifecycle from inception to production.
 * Ability to make sound engineering and business tradeoffs.
 * Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent practical experience., * Experience building Kubernetes controllers and operators (e.g., with controller-runtime or client-go).
 * Experience with ML platforms, distributed training, or workflow orchestration systems.
 * Experience operating Kubernetes or Ray across multiple clusters or regions.
 * Advanced degree (Master's or PhD) with a focus on distributed computing, system performance, or machine learning.
 * Experience contributing to open-source projects such as Kubernetes or Ray.

## Description

Leading at Uber means setting direction in motion, not waiting for perfect conditions. As a Senior Software Engineer on the Michelangelo Job Controller team, you will help build the compute infrastructure that runs ML workloads for hundreds of teams across Uber, from scheduling and launching Ray and other distributed jobs on Kubernetes to managing their lifecycle at scale. Our mission is to make transportation as reliable as running water, and we need strong engineers to build the dependable, high-performance foundation that supports that vision.

This is a role for a systems thinker who thrives in high-stakes environments. You will navigate the "messy" reality of complex infrastructure spanning global data centers and multiple cloud providers, supporting a wide variety of ML workloads. You will make smart decisions with imperfect information and own the outcomes of what you build. If you lead with clarity, build with heart, and are motivated by the challenge of reliability and efficiency at unparalleled scale, this is where you'll make your mark.

What you'll do

 * Design and build the Job Controller, the Kubernetes-native system that manages the lifecycle of ML jobs, including submission, scheduling, execution, and cleanup.
 * Build and improve the integration of Ray and Kubernetes, solving scaling, reliability, and resource utilization challenges for large training and processing workloads.
 * Work on compute infrastructure problems such as job placement across clusters, resource pools, quota, and capacity efficiency.
 * Drive reliability and observability for the controllers, including safe rollouts, failure handling, and well-defined SLOs.
 * Use data-driven analysis to diagnose job failures, scheduling delays, and performance regressions, and to optimize resource utilization.
 * Build tooling, APIs, and frameworks that increase the velocity and quality of hundreds of internal engineering and ML teams.
 * Review code and designs, mentor other engineers, and foster a culture of continuous learning and humility.
 * Work cross-functionally with ML, Product, Infrastructure, and Operations teams across the globe to drive projects and align technical investments with business value.