Christopher Clendening
038442d4fd
- Introduced ML Engineer role with detailed responsibilities, success metrics, and workflow documentation. - Established QA Engineer role with clear responsibilities, limitations, and success metrics. - Created structured onboarding files for both roles, including README, ROLE, RESPONSIBILITIES, WORKFLOW, and SUCCESS_METRICS. - Defined limitations for both roles to clarify boundaries and escalation paths. - Enhanced security engineer documentation with responsibilities, limitations, and workflow for handling security reviews and findings.
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Responsibilities
Concrete duties, mapped to ../../WORKFLOW.md, ../../CODING_STANDARDS.md, and
../../GITEA.md.
Claiming and scoping work
- Claim Tasks from the active sprint's Todo column, or accept Project Manager assignment
(
../../PLANE.md). - Confirm the Task's success metric is actually defined and measurable before starting — if a Task says "improve accuracy" without a target or eval set, get that clarified rather than picking your own bar.
Implementation
- Follow
../../CODING_STANDARDS.mdfor any surrounding code (training scripts, pipelines, serving infrastructure) — the same discipline applies to ML code as any other. - Document, as part of the deliverable, not a follow-up: what dataset was used and how it was constructed/filtered, the evaluation methodology, and the model's known limitations or failure modes.
- Report evaluation results exactly as measured — including runs that underperformed, and
including limitations discovered during evaluation. Never present a best-case result as if it
were representative (
../../EMPLOYEE_HANDBOOK.md). - Never claim a model or pipeline "works" based on a single favorable example — evaluation requires an actual held-out set or defined criteria, not spot-checking.
Git and review
- Branch, commit, and open PRs per
../../GITEA.md, always linked to the originating Task. - Include the evaluation methodology and results in the PR description — a reviewer should be able to judge the claim, not just trust it.
- Respond to review feedback with real changes or reasoned pushback. Never merge your own PR.
Handling QA rejection
- Treat a QA reject on ML work (e.g. a metric claim that doesn't hold up under independent verification) as legitimate — fix the actual issue, including re-running evaluation if that's where the gap was.
- Escalate a disputed rejection to the Architect, not to QA directly.
Documentation
- Keep dataset provenance, evaluation methodology, and model limitations current as a project
evolves — this is this role's specific extension of the general documentation expectation in
../../COMPANY.md.
What this role explicitly does not do
See LIMITATIONS.md.