Meta Machine Learning Engineer Interview Questions

I would design around batching, queueing, and routing so the hardware stays efficient without blowing up latency. Requests would enter a serving layer that groups compatible requests into micro-batches and sends them to a model fleet with version control and fallback rules. The reason I choose this structure is that throughput and cost matter almost as much as raw inference speed. I would also think about deadlines, priority tiers, and what happens when capacity is exhausted. If needed, I would discuss autoscaling and graceful degradation.

I would first measure whether the cost comes from model size, long context windows, poor batching, or low accelerator utilization. Then I would consider smaller models, distillation, prompt trimming, caching, or routing simpler requests to cheaper models. The reason I choose this approach is that cost problems usually improve when the system stops spending the same amount on every request. I would also think about whether high-risk traffic should get more expensive handling than low-risk traffic. If the interviewer wants more depth, I would discuss cost-aware routing and tiered service levels.

I would start by defining the success metric and the failure cases that matter most to the product. Then I would compare offline metrics, calibration, and segment-level performance, and validate the change with an online experiment if possible. The reason I choose this approach is that a model can improve one metric while hurting another important user outcome. I would also think about statistical significance, sample size, and guardrail metrics. If the interviewer wants more depth, I would mention that model quality should be judged on both accuracy and operational behavior.

I would assume that user input and retrieved content may be adversarial. My design would separate trusted instructions from untrusted content and enforce strict boundaries before sending data to the model. The reason I choose this approach is that prompt injection is often a trust-boundary problem, not just a model problem. I would also think about tool access control, content filtering, and logging suspicious prompts for review. If the interviewer wants more depth, I would mention red-team testing.

I would segment the traffic and compare performance across cohorts, because aggregate metrics can hide important differences. Then I would inspect whether the issue comes from data imbalance, feature mismatch, prompt differences, or domain shift. The reason I choose this approach is that fairness and quality problems often show up unevenly across users. I would also think about whether the model needs segment-specific evaluation or mitigation. If the interviewer asks, I would mention that monitoring should be broken down by the slices that matter most.