Logs, traces, and model-quality signals from hundreds of LLM products at firehose volume. The storage split that keeps queries fast, the alerting that survives drift and toxicity at scale, and why sampling has to be cost-aware and tail-biased.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Logs, traces, and model-quality signals from hundreds of LLM products at firehose volume. The storage split that keeps queries fast, the alerting that survives drift and toxicity at scale, and why sampling has to be cost-aware and tail-biased.
The seniority signal is treating this as a high-cardinality telemetry problem with AI-specific signals layered on, not a bespoke ML dashboard: an ingest pipeline feeding tiered storage (hot columnar for recent, cheap object store for cold), a query engine over both, and streaming alerting. Strong candidates flag that full logging of prompts/outputs at this scale is unaffordable, so sampling is tail-biased and cost-aware, and that AI metrics (drift, toxicity) need async scoring off the hot path. Watch for the candidate who proposes storing every token of every request forever and never costs it.
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