This was basically the whole interview compressed into one question.
Start with a high-level summary of your inference optimization experience, then dive into 1-2 specific projects where you optimized models for production. For each, describe the workload, the optimization techniques applied, and quantify the impact on latency, throughput, or cost.
Pro tip: Emphasize trade-offs: e.g., how quantization improved latency but required accuracy checks, or how batching increased throughput but added tail latency. This shows you understand real-world constraints beyond just applying techniques.
Briefly describe the model, workload characteristics (e.g., real-time vs. batch, request rate, latency SLA), and the inference platform (e.g., TensorFlow Serving, TorchServe, custom).
Explain how you profiled the inference pipeline to find bottlenecks, such as compute-bound layers, memory bandwidth, or I/O overhead.
Detail the specific techniques you used, such as quantization, pruning, knowledge distillation, operator fusion, or hardware-specific optimizations (e.g., TensorRT, OpenVINO).
Describe how you measured improvements (e.g., latency percentiles, throughput, cost per inference) and iterated on the optimizations to balance accuracy and performance.
Conclude with the overall impact (e.g., reduced latency by X%, increased throughput by Y%) and key takeaways or lessons learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They really wanted to know if I was actually measuring or just guessing.
Start by describing the inference pipeline and the specific bottleneck you identified, then explain the measurement methodology you used to diagnose it. Emphasize a data-driven approach: how you instrumented the system, collected metrics, and validated the bottleneck before optimizing.
Pro tip: Quantify the impact of the bottleneck (e.g., latency, throughput, cost) and how your measurement led to a targeted fix. Mention trade-offs you considered, showing you understand that optimizing one part can shift the bottleneck elsewhere.
Briefly outline the pipeline stages (e.g., preprocessing, model inference, postprocessing) and the scale (QPS, latency requirements) to set context.
State the specific bottleneck you found (e.g., GPU underutilization, CPU preprocessing, network I/O) and its impact on key metrics.
Detail how you measured it: profiling tools (e.g., PyTorch Profiler, Nsight), instrumentation (e.g., Prometheus, OpenTelemetry), and metrics (latency percentiles, throughput, resource utilization).
Describe how you confirmed the bottleneck (e.g., A/B tests, load tests) and prioritized it based on impact and effort.
Summarize the fix (e.g., batching, model quantization, caching) and any trade-offs (e.g., accuracy vs. latency) and results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a specific ML system you improved, quantify the latency and throughput gains with concrete metrics, and clearly explain the trade-offs you made (e.g., accuracy vs. speed, cost vs. performance). Structure your answer to show the problem, your actions, the measurable impact, and the lessons learned.
Pro tip: Uber values real-time ML at massive scale, so emphasize how your improvements impacted user experience or business metrics (e.g., reduced ETA errors, increased trip matches). Also, be honest about trade-offs—interviewers respect candidates who acknowledge downsides and explain how they mitigated them.
Briefly describe the ML system, its purpose, and the baseline latency/throughput metrics before your work. Mention scale (e.g., QPS, data volume) to highlight the challenge.
Explain what you aimed to improve (e.g., reduce p99 latency by 50%, double throughput) and any constraints like model accuracy, cost, or infrastructure limits.
Outline the technical changes you made (e.g., model quantization, caching, batching, hardware upgrades, pipeline optimization) and why you chose them.
Provide before-and-after metrics for latency (p50, p99) and throughput (QPS), and tie them to business outcomes (e.g., faster predictions, cost savings).
Explain the trade-offs (e.g., slight accuracy drop, increased complexity) and how you balanced them. Share what you learned and how you'd approach it differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frame your answer around the co-design of ML optimization and infrastructure, emphasizing that model performance goals must be balanced with resource efficiency and system constraints. Use a concrete example from your experience to illustrate how you navigated trade-offs between scheduling, autoscaling, and accelerator selection. Highlight collaboration with infrastructure teams and the metrics you used to drive decisions.
Pro tip: Show that you understand the cost implications of ML workloads at scale—mention how you quantify the dollar impact of your optimization choices, as Uber is highly focused on unit economics. Also, emphasize that you design for failure and heterogeneity, not just peak performance.
Start by defining what you are optimizing for—e.g., latency, throughput, cost, or model accuracy—and how it ties to business goals. This sets the context for infrastructure trade-offs.
Discuss how scheduling policies (e.g., gang scheduling, preemption), autoscaling triggers (e.g., queue depth, latency SLOs), and accelerator types (GPU/CPU/TPU) impose constraints on your optimization space.
Explain how you iteratively adjust model architecture, training/inference parameters, and infrastructure configurations together. For example, using mixed precision to fit larger batches on GPUs or adapting batch size to autoscaling behavior.
Share how you measure the impact of changes—e.g., A/B tests, cost per prediction, resource utilization—and validate that optimizations hold under production traffic patterns.
Describe how you productionize the solution, monitor for drift, and continuously refine based on feedback from infrastructure telemetry and business metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Had used two of the three so the comparison came naturally.
Start by listing the serving stacks you've used, then for each, explain the specific project context and the trade-offs that led to your choice. Emphasize how you evaluated factors like latency, throughput, hardware compatibility, and ease of integration, and conclude with lessons learned that would apply to Uber's scale.
Pro tip: Quantify the impact of your choices—e.g., 'vLLM reduced p99 latency by 40% compared to our previous solution'—and be honest about limitations you encountered, showing you understand real-world constraints.
Briefly name the stacks you've worked with (e.g., vLLM, TensorRT-LLM, Triton) and the scale of deployments (e.g., number of models, QPS).
For each stack, explain the use case: model types, latency/throughput requirements, hardware (GPU/CPU), and team constraints.
Detail why you chose one stack over others, focusing on trade-offs like performance, ease of use, ecosystem support, and cost.
Quantify the results: improvements in latency, throughput, cost savings, or developer productivity.
Connect your experience to Uber's scale and challenges, showing how you'd evaluate serving stacks for their ML infrastructure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Warmup question but they weren't just looking for a textbook answer.
Start by outlining the two-phase inference process: prefill (processing the input prompt in parallel) and decode (generating tokens autoregressively). Then, map each phase to its computational bottlenecks and memory access patterns, highlighting common failure points like KV cache growth and memory bandwidth limits. Conclude with practical optimizations used in production, such as batching and quantization.
Pro tip: Emphasize that inference performance is often memory-bandwidth-bound rather than compute-bound, especially during decoding. Mentioning this trade-off shows you understand real-world deployment constraints beyond theoretical FLOPs.
Explain that inference consists of prefill (processing the entire input prompt in parallel) and decode (generating one token at a time autoregressively). Note that prefill is compute-bound while decode is memory-bandwidth-bound.
Walk through the key operations: embedding lookup, multi-head self-attention (with KV caching), feed-forward networks, and layer normalization. Highlight that during decode, each new token attends to all previous tokens via the KV cache.
Discuss how KV cache size grows linearly with sequence length and batch size, causing memory pressure. Explain that decoding is memory-bound due to repeated loading of model weights and KV cache, leading to low GPU utilization.
Cover issues like out-of-memory errors from large KV caches, latency spikes with long sequences, and throughput degradation under high batch sizes. Mention optimizations: KV cache quantization, paged attention, continuous batching, and speculative decoding.
Tie the discussion to real-world deployment: trade-offs between latency and throughput, hardware selection (GPU vs. CPU), and the importance of profiling to identify bottlenecks. Mention that Uber-scale systems require efficient serving frameworks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.