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.
1.2 KiB
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>