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Jane Street·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
Jul 2026

Summary

Jane Street ML Engineer interview, one meaty technical question that was basically a full project scoped into a single session. Felt more like a design review than a coding screen.

Questions Asked (1)

Q1

Given a time-series dataset, build an end-to-end ML solution to forecast future values. Walk through your model architecture choice, how you'd implement the training loop in PyTorch, and how you'd evaluate it.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is a lot to hold in your head at once.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the forecasting problem (horizon, univariate vs multivariate, business metric) and then propose a suitable model architecture (e.g., LSTM, Transformer, or hybrid) with justification. Walk through the PyTorch training loop implementation, covering data loading, loss, optimization, and validation, and finish with an evaluation strategy that includes both statistical metrics and backtesting.

Pro tip: At Jane Street, they care about robustness and real-world performance. Emphasize how you'd handle non-stationarity, outliers, and concept drift, and discuss the trade-offs between model complexity and inference latency.

1. Clarify Requirements and Data

Ask about forecast horizon, frequency, univariate/multivariate, and business objective. Discuss data preprocessing: handling missing values, scaling, and creating time-based features.

2. Choose Model Architecture

Select an architecture (e.g., LSTM, Transformer, or hybrid) based on data characteristics and requirements. Justify choice by comparing with baselines (ARIMA, Prophet) and considering trade-offs like complexity, interpretability, and latency.

3. Implement Training Loop in PyTorch

Outline the training loop: data loading with TimeSeriesDataset, defining model, loss (e.g., MSE), optimizer (Adam), and learning rate scheduler. Include validation, early stopping, and checkpointing.

4. Evaluate and Iterate

Use time-series cross-validation (rolling window) and metrics like MAE, RMSE, MAPE, and MASE. Compare against baselines, analyze residuals, and consider business impact. Discuss deployment and monitoring.

Key Points to Mention

  • Handling non-stationarity (differencing, detrending) and seasonality (Fourier terms, seasonal decomposition).
  • Choice of loss function (e.g., quantile loss for probabilistic forecasts) and its alignment with business metric.
  • PyTorch implementation details: custom Dataset, DataLoader with time-based splits, GPU utilization, and mixed precision training.
  • Evaluation with backtesting and metrics like sMAPE, MASE, and prediction intervals.
  • Trade-offs between model complexity and inference latency, especially for real-time trading systems.
  • Monitoring for concept drift and retraining strategies in production.

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