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