The Optiver Interview (SWE)

Overview

The following article is based on a discussion with a Jane Street SWE who also received an offer from Optiver after becoming a getcracked.io member.

The Optiver Software Engineering interview is structured, fast-paced, and heavily focused on technical execution and problem solving.

Compared to firms like Jane Street, Optiver leans more toward:

• Classic technical interviews (DSA-heavy)

• Consistent evaluation across rounds

• Clear progression in difficulty

The process is typically front-loaded with multiple technical rounds, often scheduled close together.

Interview Process Breakdown

Stage 1: Recruiter Screen

Format

• ~20–25 minutes

• Short behavioral + resume walkthrough

What to Expect

• Questions about:

  • Past internships

  • Technical projects

  • Motivation for Optiver

Typical Questions

• “Tell me about your previous internship”

• “Why Optiver?”

• “What kind of work are you looking for?”

Goal

• Basic signal check (communication + background), ensuring you aren't crazy

• No deep technical evaluation

Stage 2: First Technical Interview

Format

• ~45 minutes

• Fully technical

What to Expect

• One or two coding problems

• Standard data structures & algorithms focus

Common Topics

• Arrays / strings

• Hash maps

• Basic graph / traversal

• Implementation-heavy problems

Key Insight

This round is used to filter for baseline coding ability and clarity of thought.

Stage 3: Second & Third Technical Interviews

These are typically scheduled back-to-back, making this the most intense part of the process.

Format

• 2 × 60-minute interviews

• Fully technical

• Same day

Stage 3.1: Technical Interview #2

What to Expect

• More complex problem than round 1

• Multi-step solution

• Edge cases and optimizations

Focus Areas

• Algorithmic depth

• Code quality

• Handling constraints

Stage 3.2: Technical Interview #3

What to Expect

• Highest difficulty in the process

• May involve:

  • Multiple subproblems

  • Systematic exploration of approaches

  • Performance tradeoffs

Focus Areas

• Problem decomposition

• Efficiency (time/space)

• Ability to recover if stuck