This is a massive question and I think I spent too long on the API layer before getting to the GPU scheduling piece, which is clearly where the real complexity lives.
Start by clarifying requirements and scale, then design an asynchronous pipeline that decouples request handling from GPU-bound generation. Walk through each stage—API, queue, workers, safety, storage, delivery—highlighting trade-offs and failure handling. Emphasize how you would monitor and scale the system while keeping costs manageable.
Pro tip: Treat the black-box model as a stateless service and design idempotent job processing with at-least-once delivery; this simplifies retries and ensures correctness under failures. Also, discuss how you would handle model versioning and rollbacks without disrupting in-flight jobs.
Ask about expected QPS, video length/resolution, latency SLAs, and safety requirements. Establish assumptions for peak load and cost constraints.
Sketch the end-to-end flow: API gateway, job queue, GPU workers, safety checks, storage, and CDN. Explain how components interact and why async processing is necessary.
Detail the queueing system (e.g., Kafka/SQS), GPU worker autoscaling, safety moderation (pre- and post-generation), and storage tiering (hot vs. cold). Discuss trade-offs like cost vs. latency.
Address failure modes: retries, dead-letter queues, idempotency, and graceful degradation. Explain how to scale each layer horizontally and handle backpressure.
Propose metrics (queue depth, GPU utilization, generation latency), logging, and alerting. Discuss cost optimization (spot instances, batching) and future improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a submit endpoint returning a job_id immediately, then polling or a webhook for status updates.
Start by defining the client-facing API contract, including endpoints, request/response schemas, and authentication. Then walk through the full lifecycle of a generation job, from submission to delivery, highlighting key stages, state transitions, and system components. Emphasize scalability, reliability, and user experience considerations.
Pro tip: Demonstrate awareness of trade-offs between synchronous and asynchronous processing, and how you would handle long-running jobs with webhooks or polling. Also mention idempotency and error handling to show production readiness.
Outline the endpoints for submitting a prompt, checking job status, and retrieving results. Specify request/response formats, authentication, and rate limiting.
Explain how the API receives the prompt, validates it, and enqueues a job. Mention idempotency keys to prevent duplicate submissions.
Walk through the stages: prompt parsing, model inference, video generation, post-processing, and storage. Highlight asynchronous workers and state management.
Describe how clients can poll for status or receive webhooks. Include state transitions (e.g., queued, processing, completed, failed) and error handling.
Explain how the finished video is made available (e.g., signed URL, direct download) and how resources are cleaned up after a TTL.
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Priority queues per tier, autoscaling workers based on queue depth, batching where the model allows.
Start by clarifying requirements: job types, durations, SLOs, and GPU types. Then propose a two-level architecture: a global scheduler that assigns jobs to GPU pools based on priority and resource needs, and a per-worker pipeline that manages job execution, preemption, and checkpointing. Emphasize trade-offs between utilization, latency, and fairness, and discuss how to handle heterogeneous jobs with techniques like bin packing, gang scheduling, and preemption.
Pro tip: Show awareness of real-world constraints: GPUs are expensive, so even small improvements in utilization matter. Mention that you'd instrument the system to measure utilization and job latency, and use that data to iterate on scheduling policies.
Ask about job types (training, inference, batch), typical durations, SLOs, priority levels, and GPU heterogeneity. Understand what 'highly utilized' means (e.g., target percentage) and any fairness or isolation requirements.
Propose a hierarchical scheduler: a global scheduler that places jobs onto GPU pools, and a local scheduler per pool that manages the queue and assigns jobs to individual GPUs. Consider using a central queue with priority and preemption, or a distributed approach with work stealing.
For varying durations, use bin packing to co-locate short jobs, and consider preemption for long jobs with checkpointing. Implement gang scheduling for distributed jobs to avoid deadlock and fragmentation.
Discuss techniques like oversubscription, time-slicing, and MPS/MIG for sharing GPUs. Use backfilling to fill idle time with short jobs. Monitor utilization and adjust policies dynamically.
Acknowledge trade-offs: preemption increases utilization but adds overhead; strict priority can starve low-priority jobs. Discuss fault tolerance: if a GPU fails, reschedule jobs. Ensure the system is observable and debuggable.
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Two-stage safety: classify the prompt before generation starts, then moderate sampled frames and audio after generation before making the video viewable.
Start by framing safety as a multi-layered, defense-in-depth system that spans the entire pipeline, not just a single checkpoint. Walk through the pipeline stages (input, generation, output) and describe the checks at each stage, emphasizing trade-offs between latency, cost, and safety. Conclude by discussing how you would measure and iterate on the system.
Pro tip: Emphasize that safety checks should be configurable and versioned, allowing rapid updates without redeploying the model, and mention the importance of logging and monitoring for continuous improvement.
Outline the stages: input prompt reception, preprocessing, video generation, postprocessing, and delivery. Identify where checks can be inserted without disrupting flow.
Describe checks on the prompt: content moderation (e.g., hate speech, violence), prompt injection detection, and policy compliance. Mention using classifiers, rule-based filters, and allow/deny lists.
Explain checks on generated video: frame-level and temporal analysis for NSFW content, violence, and policy violations. Use multimodal classifiers and human-in-the-loop for edge cases.
Discuss where checks live (pre-generation, during generation, post-generation) and trade-offs: latency vs. safety, false positives vs. false negatives, cost of compute.
Describe logging, metrics, and feedback loops to improve checks over time. Mention A/B testing and red-teaming.
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Object storage plus CDN, keep only metadata and a storage key in the database.
Start by clarifying requirements such as video size, user scale, latency, and budget. Then propose a scalable architecture using object storage, CDN, and adaptive streaming, and discuss cost optimization strategies like tiered storage and compression.
Pro tip: Emphasize the importance of monitoring and analytics to continuously optimize costs and performance, and mention that you would consider trade-offs between quality and cost based on user needs.
Ask about video size, number of users, geographic distribution, latency requirements, and budget constraints to tailor the solution.
Propose using object storage (e.g., S3) with appropriate storage classes (hot vs. cold) and possibly a multi-tiered approach to balance cost and access speed.
Use a CDN to cache and deliver videos globally, reducing latency and offloading origin servers. Consider adaptive bitrate streaming (HLS/DASH) for varying network conditions.
Discuss video compression (e.g., H.264, H.265), encoding ladders, and just-in-time transcoding. Use tiered storage and lifecycle policies to move older videos to cheaper storage.
Set up monitoring for CDN hit rates, storage costs, and user experience metrics. Continuously optimize based on data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said weighted fair queuing with a floor guarantee for free tier, so paid jobs get priority but free jobs can't wait forever.
Start by acknowledging the need for priority tiers to ensure paid users get better service, but emphasize fairness and system health. Propose a multi-tier scheduling system with quotas, dynamic prioritization, and anti-starvation mechanisms like aging or guaranteed minimum service for free users. Discuss trade-offs between latency, throughput, and cost, and suggest monitoring and adjusting based on feedback.
Pro tip: Mention that starvation prevention is not just about fairness but also about maintaining a healthy ecosystem of free users who may convert to paid; this shows business acumen.
Clarify the priority levels (e.g., paid, free) and their service level objectives (SLOs) such as latency, throughput, and availability. Consider business goals like conversion and retention.
Propose a scheduling algorithm that respects priorities while preventing starvation, such as weighted fair queuing, deficit round robin, or priority with aging. Include quotas and burst allowances.
Introduce guarantees like minimum GPU time for free users, aging of requests, or periodic promotion of low-priority jobs. Use preemption carefully to avoid disrupting paid users.
Set up metrics to track wait times, starvation incidents, and resource utilization. Use feedback loops to adjust weights, quotas, and thresholds dynamically.
Acknowledge trade-offs between fairness, latency, cost, and complexity. Mention alternatives like separate clusters, spot instances, or off-peak discounts for free users.
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Structure your answer as a timeline: first describe failure detection, then state preservation, then recovery and resumption. Emphasize idempotency, checkpointing, and graceful degradation to show you understand distributed systems and production reliability.
Pro tip: Mention that the system should treat the crash as a normal failure mode, not an exception—design for it with automatic retries and backoff. Also, highlight the importance of logging and metrics to diagnose the root cause without disrupting recovery.
Explain how the system detects the GPU worker crash: heartbeat timeouts, health checks, or error signals from the worker. Mention that detection should be fast and reliable.
Describe how the system saves the job's progress (e.g., checkpoints) and isolates the failed worker to prevent cascading failures. Emphasize that state should be persisted externally, not on the worker.
Outline the recovery process: the orchestrator marks the job as failed or retryable, then schedules it on a healthy worker. Mention retry policies, backoff, and possibly moving to a different GPU type if needed.
Explain how the job resumes from the last checkpoint, ensuring idempotency and avoiding duplicate work. Discuss how to handle partial outputs and consistency.
Describe post-recovery actions: logging the incident, alerting if needed, and analyzing root cause to prevent recurrence. Mention metrics like recovery time and success rate.
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Treat the original clip as a reference input, store enough metadata to reconstruct the generation context, and allow a new job to be submitted with the prior output as a seed.
Start by clarifying the current one-shot generation architecture and the desired editing capabilities (e.g., extending a clip, remixing a segment). Then propose an iterative pipeline that reuses the existing model but adds a stateful editing layer with versioning, incremental updates, and user feedback loops, while addressing latency, consistency, and cost trade-offs.
Pro tip: Emphasize idempotency and versioning from the start—edits should be reproducible and reversible, which is critical for debugging and user trust in creative tools. Also, discuss how you'd measure success (e.g., edit acceptance rate, generation latency) to show product sense.
Ask questions to understand the current one-shot system (model, latency, cost) and the desired editing features (extend, remix, undo). Identify non-functional requirements like consistency, real-time feedback, and scalability.
Propose a stateful service that stores generation history and supports incremental edits. Use a versioned graph of edits, where each edit is a transformation on a base generation, and leverage the existing model with conditioning on previous outputs.
Discuss how to maintain coherence across edits (e.g., using latent space interpolation, attention mechanisms), manage latency (caching, async processing), and control cost (batching, model distillation).
Outline how users interact with the system (e.g., timeline UI, prompt-based edits) and how feedback (accept/reject) is collected to improve the model over time.
Propose metrics (edit success rate, latency, user retention) and an A/B testing strategy to validate the feature. Discuss how to handle failures and rollbacks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Route a small percentage of traffic to the new model, gate it behind a feature flag, and cap the GPU budget allocation for the experiment.
Start by framing the problem as a cost-aware experimentation challenge: you need statistically valid results while controlling GPU spend. Propose a staged rollout with a small, well-powered A/B test, using techniques like traffic shaping, caching, and budget caps to keep costs predictable.
Pro tip: Tie your experiment design directly to a cost-per-success metric (e.g., cost per additional conversion or per quality-adjusted output) so you can justify the higher model cost with clear ROI. Also, pre-register a stopping rule to avoid burning budget on inconclusive results.
Clarify the primary metric (e.g., user engagement, task success) and the maximum acceptable GPU spend for the experiment. Set a hard budget cap and a minimum detectable effect to size the test.
Use a small but statistically powered sample, randomize at the user or session level, and consider a switchback or interleaving design to reduce variance. Pre-compute or cache expensive model outputs where possible.
Set up real-time monitoring of GPU usage, latency, and error rates. Implement automatic kill switches if spend exceeds thresholds or if the new model degrades key guardrail metrics.
Start with a small percentage of traffic (e.g., 1-5%), then ramp up only if early results are promising and within budget. Analyze results with sequential testing or Bayesian methods to allow early stopping.
Based on the cost-benefit analysis, decide whether to fully launch, iterate on the model, or abandon. Document learnings and update cost models for future experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.