Add ML and QA Engineer documentation and workflows

- 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.

employees/ml/MEMORY.md

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+# ML Engineer — Memory
+
+This role's own accumulated context: dataset quirks, evaluation gotchas, and past implementation
+judgment calls along with the reasoning behind them. Not automatically shared with other roles —
+see `../../MEMORY.md` on the two-tier memory system. Promote anything company-wide to
+`../../memory/architecture-memory.md` instead of leaving it siloed here.
+
+## Dataset and evaluation notes
+
+*None recorded yet.* Record quirks discovered in a dataset (labeling inconsistencies, class
+imbalance, known-bad samples) or an evaluation setup (a metric that's misleading for a
+particular task type) so they're not rediscovered from scratch next time.
+
+## Implementation judgment calls
+
+*None recorded yet.*
+
+```
+### YYYY-MM-DD — <short title>
+<the call made, and the situation it responded to>
+**Reasoning:** <why this approach, over the alternatives>
+```
+
+## Model/pipeline limitations discovered
+
+*None recorded yet.* A running account of known limitations found during evaluation, so they're
+tracked even after the Task that discovered them closes.
+
+## Format for new entries
+
+```
+### YYYY-MM-DD — <short title>
+
+<the observation>
+
+**Why it matters:** <what this changes about how you implement/evaluate going forward>
+```