JPMorgan Chase SDE II Interview Experience

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jpmorgan chase
· SDE II
August 15, 2026 · 1 reads

Summary

I interviewed for an SDE II role at JPMorgan Chase, completing four rounds with VPs and Senior VPs that covered system design, Python, data structures & algorithms, AI/ML, and security topics.

Full Experience

I recently interviewed for an SDE II position at JPMorgan Chase and wanted to share my experience. Before attending the interview, I searched for a detailed interview-experience blog for this role but could not find one that covered the process thoroughly. I hope this helps other candidates preparing for a similar opportunity.

The interview process consisted of four main rounds. The interviewers were VPs and Senior VPs at the company. One helpful aspect was that I knew who would be interviewing me beforehand. This allowed me to review their professional backgrounds and prepare for topics related to their areas of experience. However, the actual questions were still interesting and sometimes unexpected.

Round 1: Hiring Manager

Major topics: Python, System Design, and Data Structures and Algorithms

The interviewer focused primarily on system design.
He first asked me to choose an application I had previously worked on, design it, and explain its architecture from end to end. Since I had a strong understanding of the product I had developed, I was able to answer the follow‑up questions comfortably.
He then gave me a database table containing two columns:
user_id
region
The table contained more than one million records, and I was asked how I would optimize a query that searches for a particular entry.
This led to a detailed discussion about:
Database indexing
Table partitioning
Sharding
Replication
Trade‑offs among these approaches

After the system‑design discussion, the interviewer moved to Python fundamentals. The questions included:
Multithreading vs multiprocessing
Differences between PUT, POST, and PATCH methods
How garbage collection works
How Python handles garbage collection
Deadlocks
Other basic Python concepts

Finally, I was asked to explain the logic behind Kadane's algorithm.
Overall difficulty: Easy to medium

Round 2: VP Round

Major topics: Python, DSA, and managerial questions

The technical questions in this round included:
Parallelism versus concurrency
A real‑world use case for a stack
Mutable and immutable data types
Two logical puzzles

The discussion about the real‑world usage of a stack lasted nearly ten minutes, with multiple follow‑up questions.
The interviewer also asked some managerial and career‑related questions:
Why do you want to switch company?
Have you discussed your decision with your current manager?

This round evaluated both technical fundamentals and the reasoning behind my career move.

Round 3: Senior VP Round

Major topics: Personal projects, resume deep dive, AI/ML, RAG, and security

This round focused heavily on the projects and work experience mentioned in my resume.
The interviewer asked the following questions:
What is the difference between a skill and an agent?
What are LangChain and LangGraph?
How does a RAG architecture work? Explain it in depth.
How would you send a large file to another endpoint?
What are the different types of embeddings?
How would you perform chunking?
How are chunks stored in a database?
What preprocessing steps are used for images and text?
What is the difference between accuracy, precision, and recall?
What is the difference between authentication and authorization?
Can roles be created to restrict users from performing certain actions using tokens?
How does token‑based authorization work?
What does the architecture of a token‑based authorization system look like?
How does retrieval happen from the database in a RAG architecture?

Most of this round was driven by my resume. The interviewer went deeply into my projects and professional experience rather than asking only surface‑level questions.

Round 4: VP Round

Major topics: DSA, System Design, Python, Kubernetes, and work experience

The questions included:
Can a list be stored inside a tuple?
How does memory cleanup work?
Explain the CAP theorem.
How does scaling work in Kubernetes?
Solve a problem involving the K largest integers. Explain the elements are arranged at each step of the solution.
Explain the logic behind merge sort.

The interviewer also asked several follow‑up questions about my work experience and the systems I had developed. After completing this round, I had an additional 15‑min call with an ED.

Interview Questions (25)

1.

Optimize Query for Large User Table

System Design·Medium

Given a database table with columns user_id and region containing over one million records, how would you optimize a query that searches for a particular entry?

2.

Kadane's Algorithm Explanation

Data Structures & Algorithms·Easy

Explain the logic behind Kadane's algorithm.

3.

Parallelism vs Concurrency

Other

Explain the difference between parallelism and concurrency.

4.

Real‑World Use Case for a Stack

Data Structures & Algorithms

Provide a real‑world use case for a stack data structure.

5.

Mutable vs Immutable Data Types

Data Structures & Algorithms

Explain the difference between mutable and immutable data types in Python.

6.

Skill vs Agent

Other

What is the difference between a skill and an agent?

7.

LangChain and LangGraph

Other

What are LangChain and LangGraph?

8.

RAG Architecture Deep Dive

System Design

How does a Retrieval‑Augmented Generation (RAG) architecture work? Explain it in depth.

9.

Sending Large Files to an Endpoint

System Design

How would you send a large file to another endpoint?

10.

Types of Embeddings

Other

What are the different types of embeddings used in machine learning?

11.

Chunking Strategy

Other

How would you perform chunking of data for processing?

12.

Storing Chunks in a Database

Other

How are chunks stored in a database?

13.

Preprocessing for Images and Text

Other

What preprocessing steps are used for images and text before feeding them into a model?

14.

Accuracy vs Precision vs Recall

Other

What is the difference between accuracy, precision, and recall?

15.

Authentication vs Authorization

Other

What is the difference between authentication and authorization?

16.

Role‑Based Token Restrictions

Other

Can roles be created to restrict users from performing certain actions using tokens?

17.

Token‑Based Authorization Mechanics

Other

How does token‑based authorization work?

18.

Token Authorization System Architecture

System Design

What does the architecture of a token‑based authorization system look like?

19.

RAG Retrieval Process

System Design

How does retrieval happen from the database in a RAG architecture?

20.

List Inside a Tuple

Data Structures & Algorithms

Can a list be stored inside a tuple in Python?

21.

Memory Cleanup in Python

Other

How does memory cleanup work in Python?

22.

CAP Theorem Explanation

Other

Explain the CAP theorem.

23.

Kubernetes Scaling

System Design

How does scaling work in Kubernetes?

24.

K Largest Integers Problem

Data Structures & Algorithms

Solve a problem involving the K largest integers and explain how the elements are arranged at each step of the solution.

25.

Merge Sort Logic

Data Structures & Algorithms

Explain the logic behind merge sort.

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