ML engineers moving into FDE work should read the failures before touching the model, an FDE Times guide argues

FDE Times published a career guide on 14 October for machine learning engineers moving into forward deployed roles. Its main point is that an FDE usually doesn't train models. Citing Anthropic's FDE (Applied AI) posting, it describes the job as building production applications with an already capable model inside the customer's own systems, on data nobody has cleaned up.

The guide's core habit is error analysis before model fixes. Following Hamel Husain's advice not to write an eval rubric before looking at the data, it suggests pulling real traces, writing a short note on each failure, grouping the notes, and fixing the biggest group first. In its hypothetical insurance-email example, 9 of 14 failures come from forwarded email chains and 3 from attachments the pipeline never reads. Only 2 are genuine model errors, so a pricier model wouldn't have helped.

It also warns about three traps: blaming the model by reflex, trusting a single rerun of non-deterministic output, and reporting results to the customer as F1 scores instead of outcomes they care about, such as how many emails reached the right handler. It closes with a CV rewrite that turns a fine-tuning bullet into one that walks from real data to failures found, eval built, and result for the user.

Why it matters: ML engineers are a natural feeder pool for FDE teams, but interviewers want proof that you can make a model work on messy customer data. Showing trace-level debugging and results in business terms is a concrete way to show that.