Thomas Huang, a software engineer at LinkedIn, joined the company to work on machine learning (ML) infrastructure, which he believes is a crucial aspect of ML engineering teams' operations. He chose this role after realizing that his previous experience as a machine learning scientist was more aligned with data engineering and software engineering than actual machine learning work. Huang's new role involves working on LinkedIn's feature store, Feathr, which was recently open-sourced. The feature store allows for offline, online, and nearline operations, making it a comprehensive project. At LinkedIn, the company is using Feathr to improve its machine learning models, including those used in detecting abuse, ads, and people you may know features. Huang views the ML engineering role evolving over time, with the role blurring between data science, software engineering, and research. He believes that startups use the machine learning engineer title loosely and that roles can be outside of specific domains. In his previous role at Alectio, Huang worked on active learning as a service, which he found to be challenging due to its reliance on flawed premises. He advises students or others hoping to get into an ML engineering or ML platform type role to be patient, take alternative positions, and stay intellectually stimulated during the job search process.