5 min read
AI Job Titles Explained
AI job titles are not standardized. Two companies can use the same title for very different work, and different titles for nearly identical work. Here's a plain-language map of the main ones and what each team actually expects.
Machine learning engineer
Trains, fine-tunes, and ships models into production systems. Strong software engineering plus enough modeling depth to debug training and evaluation. This is the largest category by volume and the most common entry point from a backend or data engineering background.
LLM / applied AI engineer
Builds products on top of foundation models rather than training them from scratch: retrieval, prompting strategy, tool use, agent orchestration, evaluation, and cost control. Fastest-growing title of the last two years and the most accessible for strong product engineers.
Research scientist / research engineer
Scientists set the research agenda and publish; research engineers build the training infrastructure and run the experiments. Scientist roles usually expect a PhD or an equivalent publication record. Research engineer roles frequently do not, and are a realistic target for systems engineers who like large-scale training.
MLOps / ML infrastructure engineer
Owns the platform: training clusters, feature stores, model registries, serving, monitoring, and inference cost. Closer to platform and SRE work than to modeling. If you come from DevOps or distributed systems, this is the shortest path into AI hiring.
Data scientist and analytics engineer
Measurement, experimentation, and decision support. At AI companies this increasingly includes model evaluation and offline/online metric design, which makes it a common bridge into applied AI work.
AI product manager and AI designer
Decides what a nondeterministic system should do, how it should fail, and how users understand its confidence. Requires enough technical fluency to reason about latency, cost, and error modes, but not model training experience.
Seniority labels
Entry-level AI roles are often labeled new grad, university grad, or with a trailing I or 1. Mid-level roles usually carry no modifier. Senior, staff, and principal indicate scope rather than years. If a title has no modifier, read the requirements section rather than guessing.
Ready to apply?
Browse verified remote AI and tech roles pulled straight from company career pages, and updated daily.
Browse the AI job board