Jane Street

Typical interview loop

How Jane Street usually runs it for applied AI and LLM engineer roles.

1. 1

   Application review

   Focus: Background and role fit

   A person, not a filter, reads every application and may steer you toward a better-fitting role.

2. 2

   Recruiter screen

   Focus: Background and motivation

   A short call with a recruiter about your background and the role before any technical rounds.

3. 3

   Technical interview over Zoom

   Focus: Coding, problem-solving, communication

   You solve an open-ended coding problem with an engineer in a real language, talking it through as you go; ML candidates start on the same track as software engineers.

4. 4

   Final round: collaborative coding

   Focus: Code quality, collaboration

   In-person interviews at a Jane Street office built mostly around working through a series of coding problems with your interviewers.

5. 5

   Final round: ML modeling (ML roles)

   Focus: Modeling judgment, ML depth

   ML researchers and engineers also work with interviewers on realistic modeling problems similar to the firm's daily work.

Topics to prepare for Jane Street

The topics its loop weighs most, each with what interviewers check and what to study.

• Python for ML interviewsCoding
• Calling LLM APIs in Python: async, streaming, retriesCoding
• Embeddings basics: word2vec, contrastive learning, what embeddings captureML fundamentals
• Self-attention from scratchTransformers
• Evals and error analysisEvals
• The decoder-only transformer (GPT forward pass)Transformers
• Decoding: greedy, beam, temperature, top-k/top-pInference
• LLM-as-judge: building and validating judgesEvals

How it weights your plan

Selecting Jane Street when you build a plan multiplies each topic’s priority score: