ok so i passed the Scale AI senior system design round recently. it's a little different from what i expected based on FAANG-style prep, so figured this might help someone.
first: they care a lot about data throughput and operational correctness, not just pure distributed systems theory. Scale's core business is human-in-the-loop data labeling, so the design questions tend to map to that world. think task queues, worker assignment systems, annotation pipelines, rate limiting on human workers, QA sampling layers.
my actual prompt was something along the lines of: design a system that routes labeling tasks to the right human workers at scale, with quality assurance built in. vague on purpose. the goal is to see how you scope it.
what they looked for, from the debrief conversation: clear problem scoping upfront. i spent the first 8 minutes clarifying throughput requirements, latency targets, consistency needs a practical storage model. they pushed back on my first instinct (sql) and wanted to hear the tradeoffs between relational and columnar for the query patterns i described explicit handling of worker state and partial failures. this is where i got most of the engagement. idempotency, retry semantics, dead letter queues a monitoring and observability layer. not deep, but you need to name the things you'd instrument
for L5 specifically, they expect you to drive without much prompting and defend your choices. the interviewer acted like a junior teammate asking 'why not X' questions.
time: 60 minutes, which goes fast. i'd say 15 min scoping, 25 min design, 10 min drill-down, 10 min Q&A.
did not get a lot of CAP theorem theory questions. it's more applied than academic. prepare for systems that are actually adjacent to what they build.