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JP Morgan Chase·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior
Apr 2026

Summary

ML engineer system design round at JP Morgan Chase, one question but it had a lot of surface area. The prompt sounds straightforward until you realize how many moving parts there are between raw Reddit text and anything resembling a tradeable signal.

Questions Asked (1)

Q1

Design an end-to-end machine learning system that generates stock predictions from Reddit comments.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with data ingestion and spent probably too long on the Reddit API rate limits and scraping strategy before they nudged me toward the modeling side.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business objective and constraints, then walk through the ML lifecycle from data ingestion to deployment and monitoring. Emphasize the unique challenges of Reddit data (noise, bias, real-time nature) and how you would address them with robust engineering and modeling choices.

Pro tip: At a bank like JPMorgan, regulatory compliance and model explainability are as important as predictive performance. Proactively discuss how you'd handle PII, market manipulation risks, and audit trails.

1. Clarify Requirements and Scope

Ask about prediction horizon (intraday vs. long-term), target assets, latency requirements, and success metrics. Define the system's boundaries and integration points with existing trading systems.

2. Data Acquisition and Preprocessing

Design a pipeline to ingest Reddit comments via API, filter relevant subreddits, and handle rate limits. Clean text, remove bots/spam, and align timestamps with market data.

3. Feature Engineering and Modeling

Extract sentiment, topic, and network features from comments. Choose models (e.g., NLP + time series) and validate with walk-forward backtesting to avoid lookahead bias.

4. Deployment and Monitoring

Deploy as a scalable service with low-latency inference. Monitor data drift, model performance, and compliance; set up retraining triggers and A/B testing.

5. Risk and Compliance Integration

Incorporate explainability (e.g., SHAP), audit logging, and safeguards against market manipulation. Ensure the system adheres to financial regulations.

Key Points to Mention

  • Data quality challenges: noise, sarcasm, bots, and survivorship bias in Reddit data.
  • Feature engineering: sentiment analysis, named entity recognition, and temporal aggregation.
  • Model choice: hybrid approach combining NLP (e.g., transformers) with time-series models (e.g., LSTM, ARIMA).
  • Backtesting methodology: walk-forward validation, transaction costs, and avoiding lookahead bias.
  • Scalability and latency: stream processing (e.g., Kafka), model serving (e.g., TensorFlow Serving), and caching.
  • Regulatory compliance: explainability, audit trails, and prevention of market manipulation.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.