← Jane Street Interview Insights

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

SeniorPrefer not to say
Apr 2026

Summary

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.

Questions Asked (2)

Q1

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.

Algorithms & Data StructuresSystem DesignData Modeling
Author's notes

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

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a data model that supports efficient insertion and retrieval of listings based on price and time priority. Implement the DataStore with a priority queue or balanced BST, and write the order processing function that iterates through buy orders, matching them against the cheapest qualifying listings while tracking fills and transaction prices.

Pro tip: Emphasize the importance of tie-breaking by insertion order and discuss how to maintain that efficiently (e.g., using a monotonic counter or timestamp). Also, mention potential concurrency issues if the marketplace is multi-threaded, showing awareness of real-world system design.

1. Clarify Requirements and Constraints

Ask about expected scale, order types, partial fills, and whether listings can be modified or cancelled. Confirm the exact matching rules and output format.

2. Design the DataStore

Choose data structures that allow efficient insertion of listings and retrieval of the cheapest qualifying listing. Consider a min-heap keyed by (price, insertion_order) or a balanced BST with price as key and a queue for ties.

3. Implement Order Processing

For each buy order, repeatedly extract the cheapest listing that satisfies price <= limit. If multiple listings have the same price, ensure the earliest inserted is chosen first. Accumulate fills and compute the transaction price (likely the listing price).

4. Handle Edge Cases and Optimizations

Address scenarios like no qualifying listings, partial fills, and large volumes. Discuss time complexity and potential optimizations, such as lazy deletion or batch processing.

5. Test and Validate

Walk through examples, including ties and multiple orders, to verify correctness. Consider writing unit tests for the DataStore and the processing function.

Key Points to Mention

  • Use of a priority queue (min-heap) or balanced BST for efficient retrieval of cheapest listings.
  • Tie-breaking by insertion order: implement with a monotonic counter or timestamp combined with price in the key.
  • Time complexity analysis: O(log n) insertion and extraction, O(m log n) for processing m orders.
  • Handling partial fills and updating the DataStore accordingly.
  • Concurrency considerations if the marketplace is accessed by multiple threads.
  • Output format: map of order IDs to final transaction prices, ensuring all fills are recorded.

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

Q2

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.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

This one genuinely stressed me out.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data structure that efficiently handles timestamp-ordered processing and unit pool management. Discuss the algorithm for detecting and applying billing adjustments, and analyze time/space complexity and trade-offs.

Pro tip: Emphasize the importance of maintaining invariants (e.g., pool units are always the cheapest available) and consider edge cases like multiple adjustments for the same buyer. Also, mention how you would test the system with randomized inputs.

1. Clarify Requirements and Constraints

Ask questions to understand the exact rules: what constitutes a failed payment, how units are re-opened, what triggers refunds or charges, and any constraints on data size or real-time processing.

2. Design Data Structures

Choose appropriate data structures: a priority queue (min-heap) for the pool of re-opened units to always access the cheapest, and a balanced BST or sorted list for successful buyers to find those who could benefit from cheaper units.

3. Process Transactions in Timestamp Order

Iterate through the transaction log sorted by timestamp. For each transaction, update the state: add failed units to the pool, and for successful purchases, check if a cheaper unit is available in the pool to trigger a refund.

4. Handle Adjustments and Pool Updates

When a refund is issued, the original unit becomes available and should be added to the pool. Similarly, when an out-of-stock buyer can be served from the pool, issue a charge and remove the unit from the pool.

5. Analyze Complexity and Trade-offs

Discuss time and space complexity of the approach, and consider alternative designs (e.g., using a segment tree) and their trade-offs in terms of implementation complexity and performance.

Key Points to Mention

  • Use of a min-heap for the pool to efficiently retrieve the cheapest unit.
  • Maintaining a sorted structure (e.g., balanced BST) for successful buyers to quickly find those who can benefit from cheaper units.
  • Handling of multiple adjustments for the same buyer and ensuring correct net charges/refunds.
  • Edge cases: no pool units, multiple failed payments, out-of-order timestamps (if not guaranteed sorted).
  • Time complexity: O(n log n) due to heap and BST operations, where n is number of transactions.
  • Testing strategy: unit tests for edge cases, randomized testing for correctness, and performance testing for large inputs.

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