Design a GPU Test and Validation Platform

Clarified what's under test (silicon bring-up vs firmware/driver vs ML workload) and fleet scale before diving into design
Established functional scope: submission, scheduling, execution, results, health/quarantine, reporting
Defined non-functionals with concrete numbers (fleet size, throughput, latency, storage/retention)

Proposed a decoupled pipeline: API/job service → queue → scheduler/device matcher → agent on node → results ingest/storage
Distinguished device inventory/health store from job state store from results store, with rationale for each storage choice
Included an artifact store separate from structured results DB with tiering for cost
Defined clear APIs for job submission, status polling, result reporting, and device health/quarantine

Addressed device allocation correctness (avoiding double-booking, lease/TTL on reservations, handling agent crash)
Addressed flaky test vs flaky hardware differentiation with a concrete mechanism (e.g., re-run on different device, failure-signature correlation)
Addressed scheduler scalability/fairness (priority queues, sharding by device pool, quota)
Discussed result immutability/traceability (environment fingerprinting, idempotency, tamper resistance)
Articulated at least one explicit trade-off with reasoning rather than just stating a choice

Structured the answer through a clear requirements → architecture → deep-dive progression within time budget
Proactively distinguished this problem from generic CI/CD (hardware can genuinely fail, exclusivity matters)
Used concrete numbers/scale assumptions to justify design decisions rather than staying abstract

Problem