Software Systems Engineer, Data Labeling Engineering - Early Career

Job Description About the Team   Help teach our self-driving vehicles how to see and understand the world.   The   Data Labeling Engineering   team designs, builds, and   operates   hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors'   AV organization . We   operate   in the intersection of   software engineering ,   data engineering , and   AI/ML , defining the strategies, tooling, and quality controls that create reliable training data at scale. Our tools and platform are used by thousands of users and consumers.   We own a modern full ‑ stack architecture including   TypeScript/React, Python,   GraphQL , Golang , and   ML model services , which powers data ‑ annotation pipelines and machine ‑ led training data solutions at   foundation ‑ model scale . We partner closely   across   AI/ML engineers ,   Product Operations ,   Product Management ,   Data Science , and other   ML Platform   groups.   About the Role   As an early-career Software Engineer on the Data Labeling Engineering team, you will build tools and services that help machine learning teams create high-quality training data for autonomous driving. Your work may span frontend experiences, backend services, data pipelines, machine learning integrations, and quality systems used by labelers, ML engineers, and operations teams.   This role is designed for a recent college graduate or engineer early in their career   who wants to own meaningful pieces of a platform, grow their technical   expertise , and work directly on systems that enable the next generation of AV capabilities. You will learn from experienced engineers while contributing to production systems and developing depth across frontend, backend, data, and ML-adjacent technologies.   What   You’ll   Do   • Level up how ML teams work with data   Develop automation and tooling that give ML engineers deep insight into labeling workflows and data quality (e.g., efficiency dashboards, auto ‑ QA, autolabel review tools), reducing iteration time from idea to trained model.   • Apply ML to   labeling   itself   Collaborate with ML engineers to design and integrate ML ‑ driven data annotation (pre ‑ labeling,   autolabeling , active learning loops), helping us move from human ‑ only to machine ‑ led labeling at scale.   • Build high ‑ impact labeling experiences   Design, implement, and test scalable, high ‑ performance user experiences and services using modern full ‑ stack and/or frontend technologies.   You’ll   ship features spanning multiple   surface-areas   that directly affect how quickly and accurately we can label data for new models and cities.   • Champion AI ‑ assisted engineering   Use and advocate for modern AI ‑ powered development workflows (code assistants, automated documentation, test generation, etc.) to increase build-velocity while   maintaining   code and product quality.   Basic Qualifications   • Recently   completed a bachelor’s, master’s, or PhD degree in   Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Machine Learning, or a related   STEM field.  For completed degrees, graduation must have occurred within the past   12   months.     • Experience   shipping   software or features through internships, research, academic projects, or   prior   professional work.   • Programming experience in one or more languages such as   Python, TypeScript, JavaScript, Go, Java, or C++.   • Familiarity with software fundamentals, including o bject-oriented design, design patterns, data structures, algorithms, API/interface design , and engineering best practices.   • Strong   communication   and collaboration   skills; you can explain tradeoffs, influence peers, and work t