
Citadel AI Engineer case interview: real-time multimodal market-signal ML pipeline
This case simulates partnering with a PM and research engineers at Citadel to design and defend an end-to-end AI/ML pipeline that ingests live market microstructure data and unstructured text (news/headlines/filings) to surface short-horizon tradeable signals. The interview mirrors Citadel’s bar for rigor, speed, and precision, and emphasizes practical ML for markets, systems performance, and risk-aware evaluation. What you’re given: - Data sketch: historical L1/L2 market data (trades/quotes with microsecond timestamps), corporate action/earnings calendars, and a stream of headlines/snippets; limited sample feature store; simulated transaction-cost model. - Constraints: near–real-time inference (sub-10 ms per symbol per tick budget), strict no-lookahead guarantees, memory/CPU/GPU limits, and a requirement for reproducibility under versioned data. What you’re expected to do: 1) Problem framing and target definition - Choose a concrete prediction target (e.g., 1–5 minute forward mid-price move or risk-adjusted return) and justify horizon vs. latency trade-offs. - Identify leakage risks (e.g., as-of timestamps, split/earnings timing, stale book updates) and propose time-aware CV (rolling/blocked) splits. 2) Feature and model strategy - Engineer microstructure features (order-book imbalance, spread/volatility regimes, queue dynamics) and text features (lightweight transformer embeddings or headline classifiers) with clear latency budgets. - Compare model families (gradient boosting, linear with interactions, lightweight sequence/transformer) and argue for simplicity vs. capacity under live constraints. 3) Evaluation and risk controls - Specify metrics beyond accuracy: IR/Sharpe, hit-rate conditioned on spread/vol buckets, turnover, drawdown, capacity, slippage/fees sensitivity. - Design a leak-proof backtest, walk-forward simulation, and guardrails (exposure limits, kill-switches, anomaly detectors for feed breaks and regime shifts). 4) Systems and performance engineering - Propose a streaming architecture (ingest → feature calc → inference → decision) with caching, vectorization, and parallelism; discuss Python/C++ interop, batching, and GPU/CPU trade-offs. - Do back-of-the-envelope throughput/latency math, outline profiling strategy, and suggest failure-mode handling at market open. 5) Code and debugging on-the-spot (lightweight) - Write or review a compact function to compute rolling order-book imbalance on time-indexed data without O(n^2) scans; discuss complexity, numerical stability, and unit tests. 6) Productionization and monitoring - Versioned data/model artifacts, reproducible runs, feature-store hygiene, model drift/decay monitoring, and A/B or shadow deployment into paper trading. Citadel-specific focus areas - Depth of reasoning under adversarial questioning, data hygiene and anti-leak vigilance, crisp quantitative communication, and ownership mindset. - Expect to justify every assumption with numbers, challenge hidden biases in the data, and prioritize reliable, performant designs over fashionable models.
8 minutes
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About This Interview
Interview Type
PRODUCT SENSE
Difficulty Level
5/5
Interview Tips
• Research the company thoroughly
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