real time

Severe class imbalance, a tight latency budget, and an adversary who keeps adapting. What interviewers watch for: treating imbalance honestly, setting the operating point from costs, and building for the feedback loop. This covers the end-to-end design.

Ads ranking is where calibrated probabilities collide with tight latency and money. The signal is knowing that CTR has to be calibrated (not just ranked), the feature and serving design, and the auction context. Here is the design that goes past 'train a classifier.'

Dynamic pricing combines demand forecasting, optimization, and real-time serving, under fairness and trust constraints. The signal is the predict-then-optimize structure plus guardrails against perverse outcomes. Here is the design.

The trap is swapping the classifier for an LLM. The real-time, imbalance, and adversarial constraints stay put. The signal is a hybrid: a fast calibrated model scores inline, LLMs investigate the gray zone off the hot path.

ETA is spatiotemporal regression served in real time over conditions that shift by the minute. The signal is feature freshness, segment or stage decomposition, and the asymmetric cost of being wrong. Here is the design.

Spam detection is adversarial, imbalanced classification under a low-latency bar. The interviewer is watching whether you set the threshold by cost asymmetry and rely on signals spammers cannot fake. Here is the design.

AV perception is safety-critical, real-time, multimodal ML: detect and track everything around the vehicle across multiple sensors. The signal is sensor fusion plus detection/tracking under a hard latency budget and a fail-safe bar. Here is the design.

Cleaning noise out of speech is a tradeoff between how good it sounds and how fast it runs. A strong answer weighs spectrogram masking against waveform models under a real-time budget, names PESQ and STOI as the metrics, and stays honest about the artifacts each approach brings.

A rider taps request and within seconds a nearby driver gets assigned. The interesting problems are geospatial indexing of moving drivers, the matching objective (nearest is not always best), and handling the race where two riders want the same car. Here is how to build it.

Fraud is well under 1% of transactions, the labels land weeks late as chargebacks, and the fraudsters deliberately adapt to your model. Optimizing accuracy hands you a model that approves everything. The interview is about dollars-at-risk thresholding, label delay, and a review queue with a fixed headcount.