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

SeniorPrefer not to say
Jun 2026

Summary

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.

Questions Asked (1)

Q1

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.

System DesignTechnical Trade-offsRoot Cause Analysis
Author's notes

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

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope of the language and the role's ML focus, then outline a modular OO design (lexer, parser, AST, interpreter/compiler) and a staged pipeline. Emphasize debugging through incremental testing, logging, and root-cause analysis, and tie back to how ML engineers can leverage such a system for DSLs or model configuration.

Pro tip: Show awareness that at Jane Street, correctness and clarity trump cleverness—design for testability and explain how you'd use property-based testing to catch edge cases in the compiler.

1. Clarify Requirements and Scope

Ask about the language's features (e.g., arithmetic, conditionals, functions) and the expected output (interpreted vs. compiled). Relate it to ML use cases like defining model architectures or data pipelines.

2. Object-Oriented Design

Propose classes for Token, Lexer, Parser, AST nodes (using polymorphism), and a Visitor pattern for traversal. Highlight extensibility and separation of concerns.

3. Compilation Pipeline Stages

Walk through lexing, parsing, semantic analysis, optimization, and code generation/interpretation. Explain how each stage transforms the representation and handles errors.

4. Debugging and Error Handling

Describe strategies like unit tests per stage, error recovery in parsing, logging intermediate representations, and using a debugger. Mention root-cause analysis by isolating stages.

5. Trade-offs and ML Integration

Discuss trade-offs (e.g., tree-walking vs. bytecode, simplicity vs. performance) and how the language could serve as a DSL for ML experiments, enabling rapid iteration.

Key Points to Mention

  • Use of design patterns: Visitor, Interpreter, Composite for AST
  • Pipeline stages: lexical analysis, syntax analysis, semantic analysis, optimization, code generation/interpretation
  • Error handling: panic mode recovery, error nodes, and meaningful messages
  • Testing: unit tests for each component, integration tests, property-based testing
  • Trade-offs: performance vs. development time, memory vs. speed, static vs. dynamic typing
  • ML relevance: custom DSLs for feature engineering, model configuration, or experiment tracking

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