For engineers
Builds agentic systems and tool-use that hold up in production. Here is what the role is, what it pays, and what is open now.
An applied AI engineer builds the systems around a model: agents, tool-use, retrieval, and orchestration. The model is one part. The job is making the whole thing reliable enough to put in front of real users.
The demo of an agent is easy. The version you can trust with a customer’s money is not. Applied AI engineers spend their time on the second one, which is mostly the unglamorous work of failure handling and measurement.
In the US, total compensation for strong applied ai engineers usually lands around $180k to $260k, higher at frontier labs and with equity at startups. We do not post a role we would not take ourselves.
An engineer who builds production systems on top of language models: agents, retrieval, tool-use, and the orchestration that makes them reliable.
No. ML engineers often train and serve models. Applied AI engineers build the product and systems around models that already exist.
In the US, total compensation typically runs $180k to $260k for engineers who have shipped reliable agentic systems.