← Mistral AI Interview Insights
Start by clarifying requirements and constraints (latency target, hardware, PDF structure), then propose an asynchronous API that returns a job ID and streams results. Design a three-stage pipeline (split → OCR → assemble) with bounded queues, parallel CPU splitting, batched GPU OCR, and backpressure to maximize throughput and minimize end-to-end latency.
Pro tip: Emphasize that the real bottleneck is often the GPU OCR stage, so batching and overlapping I/O with compute are critical; also mention that returning pages incrementally as they complete can dramatically improve perceived latency.
Ask about latency targets, hardware (CPU cores, GPU memory), PDF characteristics (text vs. scanned), and whether partial results are acceptable. This shapes the entire design.
Propose an asynchronous API: POST /convert returns a job ID, and the client polls or subscribes via WebSocket/SSE for page results. Include options for streaming pages as they become ready.
Split the PDF into pages using a process pool (CPU-bound), feed pages into a bounded queue, and have a GPU worker batch pages for OCR. Use a second queue for OCR results and an assembler that emits pages in order.
Parallelize splitting across CPU cores with multiprocessing; batch OCR requests on the GPU with dynamic batching to maximize utilization. Tune batch size based on GPU memory and latency requirements.
Use bounded queues to prevent memory blowup; implement backpressure so fast stages don't overwhelm slow ones. Track page order and buffer out-of-order results to deliver a consistent stream.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then design an asynchronous job processing system with a clear API for submission and result retrieval. Break down the pipeline into stages, each with its own worker pool and autoscaling, and address cross-cutting concerns like backpressure, fairness, retries, and partial failures. Emphasize trade-offs and how you would validate the design.
Pro tip: Proactively discuss how you would handle partial failures in multi-page jobs by checkpointing progress and allowing resumption, and how you would ensure fairness across tenants using per-tenant queues or rate limiting. This shows you think about real-world operational challenges.
Ask questions to understand expected scale, latency SLAs, tenant isolation needs, and failure semantics. This ensures your design meets the actual needs.
Define endpoints for job submission (returning a job ID) and result retrieval via polling or webhooks. Outline the job states (e.g., pending, processing, completed, failed) and how clients are notified.
Break the job into stages (e.g., fetch, process, aggregate) and assign dedicated worker pools per stage. Explain how stages communicate (e.g., queues) and how autoscaling works per stage based on queue depth or CPU.
Describe mechanisms for backpressure (e.g., bounded queues, rate limiting), fairness across tenants (e.g., per-tenant queues, weighted fair queuing), retries with exponential backoff, and handling partial failures (e.g., checkpointing, idempotency).
Summarize key trade-offs (e.g., polling vs webhooks, complexity vs scalability) and how you would test and monitor the system (e.g., load testing, metrics, tracing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.