Staff Machine Learning Engineer
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About The Position
The Staff Machine Learning Engineer will play a pivotal role in driving the vision and execution of LiftIQ, a Marketing Experimentation and Optimization Platform at General Motors. This position requires a technical leader with expertise in building scalable Machine Learning data products focused on experimentation and optimization. The engineer will be responsible for designing and implementing statistical and machine learning models that leverage both first-party and third-party data sources. Collaboration with cross-functional teams, including engineering, data science, and UX/UI design, is essential to refine the platform that enables effective experimentation, measurement, and optimization. In this role, the Staff ML Engineer will work closely with stakeholders from GM's various vehicle brands, subscriptions, and customer care products, as well as the Performance Driven Marketing team. The goal is to ensure business acceptance of models, metrics, and visualizations that demonstrate experiment performance and optimization. The engineer will develop platforms, tools, and capabilities that empower stakeholders and data scientists to identify marketing initiatives with high return on investment. The team is dedicated to delivering consumer-centric, personalized solutions that will help GM navigate its transition to electric vehicles (EVs). The Staff ML Engineer will also be responsible for designing and engineering efficient and resilient ML platforms and software products that can operate at scale. This includes participating in design, architecture, and code reviews, fostering collaboration, and guiding the team through technical challenges. The engineer will raise the bar for ML engineering by improving best practices, producing exemplary code, documentation, automated tests, and thorough monitoring. A strong understanding of business context and functional domain expertise in experimentation systems related to marketing and customer engagement is crucial for driving incremental value.
