Jane Street Machine Learning Engineer interview questions

10 Machine Learning Engineers have shared notes from their Jane Street interviews, with the questions they were asked in each round. That is 19% of the 54 Jane Street interview notes on Screna AI.

10 Machine Learning Engineer interview notes

Jane Street·Machine Learning Engineer·Technical Phone Screen

Jul 2026
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.
  • 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.

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

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Jane Street·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Jane Street ML Engineer interview, one big technical question that basically covered everything you'd ever want to know about time series forecasting. It was a lot to hold in your head at once.
  • You're given a time-series dataset with timestamps and numeric targets. Walk through your full pipeline: preprocessing, modeling choices, evaluation strategy, and how you'd handle concept drift over time.

“This question is basically five questions glued together and they let you run with it.”

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Jane Street·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Jane Street ML Engineer interview with a pretty gnarly systems design question. Not the typical ML theory stuff I prepped for, which threw me off a bit. The question was open-ended enough that I kept second-guessing my own design choices throughout.
  • Design and implement a compiler or interpreter for a simple custom programming language. Walk through your object-oriented design, the compilation pipeline stages, and how you'd approach debugging and fixing errors in the system.

“This one took me a minute to even figure out where to start.”

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Jane Street·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Coding round for an MLE role at Jane Street. One question, but it was a lot more implementation-heavy than I expected from a finance firm.
  • Build a simplified code editor (think VSCode-style) that supports collapsing and expanding code blocks. For example, given a 5-line snippet where lines 2 through 4 form a for-loop block, calling shrink(2) should collapse that block down to just its header line, resulting in a 3-line view.

“I spent the first few minutes just trying to figure out the right data structure.”

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Jane Street·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Jane Street ML Engineer interview, system design round focused entirely on building a production-grade time-series forecasting platform from scratch. One very long question that basically covers everything you'd need to know about MLOps. Felt like a PhD thesis defense more than a typical interview.
  • Design a complete end-to-end training framework in PyTorch for time-series forecasting, covering data ingestion with windowed sampling and feature generation, config management and reproducibility, training loop with mixed precision and gradient clipping, early stopping and checkpointing, hyperparameter tuning, experiment tracking, model registry with promotion gates, batch and streaming inference with latency SLOs, feature parity between train and serve, monitoring with drift detection and automated retraining triggers, and a full testing and CI/CD strategy. Also describe the high-level module diagram and key interfaces between components.

“This was basically the whole interview.”

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Jane Street·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026
Coding round at Jane Street for an ML Engineer role. One problem, but it had enough edge cases to keep me busy for the whole session. The board simulation stuff felt almost like a puzzle game at first, then got annoying fast.
  • Simulate a 2D game board where pieces are always inserted at the bottom of a column, pushing existing pieces up. After each insertion attempt, report whether it succeeded, whether any fully-filled column is uniform in piece type, and whether any fully-filled row is uniform in piece type.

“My first instinct was to just brute-force scan the whole board after every operation and I said so out loud.”

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Jane Street·Machine Learning Engineer·Onsite - Coding / Algorithms

May 2026
Jane Street ML Engineer interview with a coding round that went deeper into system design territory than I expected. The Connect-N problem sounds like a toy game until they start asking about infinite boards and complexity tradeoffs.
  • Design and implement a Connect-N game on an infinite board where pieces fall under gravity. Implement an insert function and a check for N-in-a-row across all four directions after each move.
  • After placing a piece, how do you efficiently check for N-in-a-row without rescanning the entire board?
  • What is the time complexity per insert, and how would you handle very large values of N efficiently?

“My first instinct was a 2D array and I almost said it out loud before catching myself.”

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Jane Street·Machine Learning Engineer·Technical Phone Screen

May 2026
Jane Street ML Engineer interview with a meaty coding problem centered on building a code folding system from scratch. The problem was well-specified but had enough edge cases in the visibility logic to keep you busy for a while.
  • Implement a CodeEditor class that supports indentation-based code folding: a render() method that shows visible lines with display numbers and block-header markers, plus shrink(d) and expand(d) operations that collapse or restore blocks based on the display line number from the most recent render.

“This took me longer to get right than I expected.”

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Jane Street·Machine Learning Engineer·Technical Phone Screen

May 2026
Jane Street ML Engineer interview with a coding problem that looks like a toy game but has real algorithmic teeth. The unbounded board constraint is what makes it interesting and also where I think most people slip up if they're not careful about complexity.
  • Implement a variant of Connect Four on an unbounded board (columns indexed by any integer, infinite vertical capacity). Support an insert(col, player) operation that drops a piece and returns true if the move creates N consecutive pieces in a row or column for that player. Each insert must run in O(N) time, not scan the whole board.

“The 'unbounded in both directions' part is the real constraint.”

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Jane Street·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Jane Street ML Engineer interview, got hit with a pretty involved marketplace simulation problem split into two parts. The second part especially had a lot of moving pieces and I kept second-guessing my pool management logic. Not the kind of question you can brute-force your way through.
  • Design and implement a DataStore for a marketplace, then write a function that processes buy orders against it: each order fills against the cheapest qualifying unsold listing at or below the buyer's limit price, with ties broken by insertion order. Return a map of filled orders to their final transaction prices.
  • Implement an end-of-day reconciliation system that processes a transaction log in timestamp order, maintains a pool of re-opened units from failed payments, and produces billing adjustments (refunds or charges) when cheaper units become available for successful buyers or when out-of-stock buyers can now be served from the pool.

“The DataStore part was fine, basically a wrapper with insert and getAll.”

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