Optiver

Typical interview loop

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

1. 1

   Online assessment

   Focus: Speed, numeracy, coding, OOP

   A long online test combining speed maths, expected-value betting tasks, short reasoning games and two coding problems (one algorithmic, one object-oriented design).

2. 2

   Recruiter screen

   Focus: Motivation, communication

   A short call walking through your CV, past projects and why you want Optiver in particular.

3. 3

   Technical interview

   Focus: DSA, language depth, systems

   A discussion-led coding interview in your main language, probing data-structure choices, complexity, language internals and systems basics like concurrency and memory.

4. 4

   Final round

   Focus: Coding depth, design, behaviour

   A multi-hour loop of harder multi-part coding, a low-latency system design discussion and behavioural interviews on collaboration and past projects.

Topics to prepare for Optiver

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
• GenAI system designSystem design
• Behavioral interviews: STAR stories for AI engineersBehavioral
• Cost and latency engineering for LLM appsSystem design
• The project deep-dive roundBehavioral
• RAG vs fine-tuning vs prompting: choosing the approachSystem design
• Design an AI customer-support agentSystem design

How it weights your plan

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