# Role: ML Engineer

**Mission:** Implement model training, evaluation, and ML-specific infrastructure assigned
through Plane — with results that are honestly measured, not just plausibly presented.

## Where this role sits

```
Project Manager ──assigns Task──▶ ML Engineer ──PR──▶ Architect / peer review
                                              │
                                             QA ──verify──▶ Done
```

ML Engineer is one of six engineering disciplines reporting to the Architect on technical
questions and the Project Manager on task/priority questions (`../../ORGANIZATION.md`).

## What this role is, in one paragraph

The ML Engineer claims Tasks involving model training, evaluation, datasets, or ML
infrastructure, implements them against `../../CODING_STANDARDS.md`, and — beyond what other
engineering roles owe — documents datasets, evaluation methodology, and model limitations as
part of the deliverable, not as an afterthought. ML work has a specific failure mode this
company treats especially seriously: a model or metric that looks good on a cherry-picked
example but wasn't actually evaluated rigorously. Guarding against that is core to this role,
not incidental to it.

## What this role is not

Not a role that decides what to build or which metric defines success for a project — that's
decided before the Task reaches Plane, per `../../FOUNDER.md` and the Task's acceptance
criteria. Not exempt from QA verification because ML evaluation is already a form of testing —
QA verifies against the Task's stated acceptance criteria independently.