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Morgan Stanley

Global investment bank with a big in-house tech org applying ML to finance

Target this company

Interview in a few hours? A quick sprint for Morgan Stanley

AI in interviews

low confidence

No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.

Checked 23 Oct 2026Source

Typical interview loop

How Morgan Stanley usually runs it for applied AI and LLM engineer roles.

  1. Recruiter screen

    Focus: Background, motivation

    A roughly 30-minute call on your ML background, your interest in finance and how your goals fit the team.

  2. Online test / recorded video (mostly campus)

    Focus: Coding basics, communication

    Early-career and campus applicants usually take a timed HackerRank coding test with no interviewer, and many roles also use a one-way recorded video screen; experienced hires often skip the test.

  3. Technical screen

    Focus: Coding, ML and math theory

    A live call, either coding in a shared editor with an engineer or a conceptual check on linear algebra, statistics and how common ML models work.

  4. Superday / onsite rounds

    Focus: ML depth, problem solving

    Four or five back-to-back interviews with engineers and managers that mix technical depth (ML techniques, algorithms, applied finance problems) with behavioral questions.

  5. Hiring manager round

    Focus: Ownership, communication

    A closing conversation with the manager on how you handled hard projects, prioritised under deadlines and explained results to non-technical people; some loops add a short case or presentation.

How it weights your plan

Selecting Morgan Stanley when you build a plan multiplies each topic’s priority score:

ML fundamentals+30%
Coding+10%
Behavioral+10%
System design-10%

Official prep and resources

Public candidate write-ups

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