AI Job Titles Explained

AI titles are useful search terms, not universal occupational standards. Google DeepMind’s careers page describes Research Scientists as identifying research questions and evaluating models, while Research Engineers bridge theory and implementation by building systems to test ideas. The same page describes Software Engineers as building reliable software, Product Managers as setting roadmaps and translating AI capabilities into product specifications, and Technical Program Managers as coordinating technical programs from research to production. These are Google DeepMind’s role descriptions; other organizations may divide work differently. Other current postings add more variation. An Amazon Senior Machine Learning Engineer posting on the Foundational AI team describes supporting RL Gym development, assessing those environments for frontier-model advancement, and building techniques for large language models in an applied research role. An AWS applied-solution posting emphasizes designing and implementing solutions with customers. MLOps work may sit under ML engineering, platform engineering, data engineering, or a dedicated operations title. AI ethics and responsibility work may appear in policy, research, governance, product, or engineering teams. The title alone does not guarantee that someone will build models, own strategy, manage people, need a specific degree, or work at a particular seniority. When evaluating an opening, compare daily responsibilities, technical ownership, partner teams, required evidence, and how success is measured. Ask which parts of the lifecycle the role owns and how it interacts with research, product, and infrastructure. Candidates can target titles to find roles, then use the posting and interview conversation to determine fit. A precise understanding of the actual work is more reliable than assuming a label carries the same meaning everywhere.