Meta publishes guidance about its engineering interviews, but it is written at a “Meta engineering loop” level, not a guaranteed script for every role. The reliable takeaway is that Meta expects a set of 45 minute conversations, often described as 4 to 6 interviews, and the core types are coding, design, and behavioral, while the exact mix and emphasis can change by role and recruiter direction.
For Enterprise AI Forward Deployed Engineer work, that “it depends” matters because the role is usually closer to applied delivery than a standard product Software Engineer interview target. So for prep, we separate what is publicly documented from what is commonly reported by candidates, and we treat anything else as an inference until a recruiter confirms it for your specific loop. Let’s sort those buckets before we practice anything.
Let’s compare what is official versus what varies, so we do not overfit our preparation plan to rumors or to a different role’s process.
Enterprise AI Forward Deployed Engineer work is delivery work that sits between a product team and a partner or customer team. Forward Deployed Engineer (FDE) means an engineer who works directly with external teams to get a product working in the real environment, then feeds what they learn back into product and engineering.
Consider this situation. A large retailer wants to use a Meta open model for an internal assistant that answers policy questions, but their data is in a locked down knowledge base and their security team requires strict access control. Your job as the FDE is to make the first working version real, prove it is safe enough to evaluate, and turn unknowns into decisions the customer can sign off on.
In that kind of engagement, you spend a lot of time on scoping and on reducing risk. Discovery means the structured set of questions and tests you run to learn the customer’s goals, constraints, data, and success criteria before you commit to a build plan. If you skip discovery, you often end up solving the wrong problem fast and then paying for it in rework.
A lot of Enterprise AI delivery comes down to choosing the right technique for grounding the model in customer data. Retrieval-augmented generation (RAG) is a pattern where the system looks up relevant documents at question time and gives them to the model, so answers can cite the customer’s content without retraining the model. In our retailer example, RAG is usually the first thing to test because it is fast to prototype and easier to control than training.
Meta’s official guidance gives you the broad categories, but it does not promise a fixed order or a fixed set for every role. Candidate reports and third party summaries often describe a sequence that starts with a recruiter screen, then technical rounds like coding and design, then behavioral, sometimes with a newer AI-enabled coding format, and finally a team matching step if you pass. Treat that sequence as a working hypothesis, then confirm the real one with your recruiter.
The point of mapping the loop is not to predict the calendar. It is to understand the skill signals each round is trying to pull out of you, so your prep matches the job. For an Enterprise AI FDE, those signals often include the ability to clarify messy requirements, design a safe and testable plan, and communicate trade offs under time pressure while still coding cleanly.
Let’s pin down that commonly reported round map in a way we can use for preparation, without treating it like a promise.