Optiver Interview Guide: Process, Questions, and Preparation

Optiver is one of the world’s leading proprietary trading firms, operating at the intersection of markets, technology, and quantitative decision-making. Interviews at Optiver are designed to reflect that environment. They test whether you can think precisely, reason probabilistically, and make correct decisions under time pressure where small mistakes have outsized consequences.

If you are preparing for an Optiver interview, this guide walks you through what to expect across the process, from early screening to final rounds. You will learn how Optiver evaluates candidates across engineering, data, research, and product roles, what interviewers prioritize at each stage, and how to prepare in a way that aligns with Optiver’s fast-moving, correctness-driven culture.

Use this parent guide to understand Optiver’s overall interview philosophy and structure, then go deeper with the role-specific guides below:

• Optiver Software Engineer
• Optiver Data Engineer
• Optiver Machine Learning Engineer
• Optiver Data Scientist
• Optiver Research Scientist
• Optiver Data Analyst
• Optiver Product Manager

Optiver operates in highly competitive, real-time markets where speed, accuracy, and probabilistic reasoning directly translate into profit and loss. Unlike consumer technology companies, Optiver does not optimize for user growth or engagement. It optimizes for decision quality under uncertainty.

Across roles, Optiver looks for candidates who can reason quantitatively, stay calm under pressure, and continuously refine decisions based on feedback from the market.

Probability-first thinking

Probability and expected value are foundational at Optiver. Interviewers expect candidates to be comfortable reasoning about uncertainty, distributions, and trade-offs rather than relying on deterministic logic.

Strong candidates demonstrate:

• Comfort with probabilistic outcomes rather than single-point answers
• Clear reasoning about risk and variance
• Willingness to update beliefs when new information arrives

Answers framed as absolutes without uncertainty signals tend to underperform.

Optiver signal: Correct probabilistic reasoning beats confident guesses.

Speed with discipline

Optiver values speed, but not at the expense of correctness. Interviews often simulate situations where you must reason quickly without cutting logical corners.

Interviewers listen for:

• Clear assumptions stated early
• Logical shortcuts that remain valid
• Awareness of when speed introduces risk

Candidates who rush without validating logic are penalized more heavily than those who slow down to ensure correctness.

Optiver signal: Fast and correct beats fast and clever.

Feedback-driven improvement

Optiver teams operate with tight feedback loops. Decisions are evaluated constantly, and improvement is expected.

In interviews, this shows up through questions about:

• How you respond when results differ from expectations
• How you learn from incorrect assumptions
• How you iterate on models, systems, or strategies

Candidates who can explain how their thinking evolved tend to stand out.

Optiver signal: Learning speed matters as much as initial performance.

The Optiver interview process is designed to answer three core questions:

1. Can you reason accurately under time pressure?
2. Can you work with uncertainty using probabilistic thinking?
3. Can you execute reliably in fast-moving, competitive environments?

The exact process varies by role and location, but most Optiver interviews follow a consistent structure.

Optiver Interview Stages at a Glance

Below is a closer look at how these stages typically work.

Application and recruiter screen

Optiver hiring is selective and role-specific. Recruiters look for candidates who demonstrate strong analytical foundations, not just familiarity with tools or languages.

Early conversations focus on:

• Your background and problem-solving experience
• Why Optiver and why this role
• Expectations around pace and performance

Overly polished or generic answers can signal misalignment.

Tip: Practice concise explanations using the AI interview tool.

Initial technical screen

The first technical screen evaluates baseline reasoning ability. Depending on the role, this may include:

• Probability and expected value problems
• Logic and mental math questions
• Coding or data manipulation tasks

Interviewers care deeply about how you reason through uncertainty.

Tip: Practice role-aligned questions in the Interview Query question bank.

Deep technical rounds

Later rounds focus on depth, correctness, and adaptability. Interviewers may change assumptions mid-problem or introduce new constraints.

You may be asked to:

• Recompute outcomes under different probabilities
• Identify flawed assumptions
• Explain why an approach fails under edge conditions

These rounds are intentionally demanding.

Optiver signal: Calm, structured reasoning under pressure.

Behavioral and judgment interviews

Behavioral interviews at Optiver are designed to evaluate decision-making under pressure, learning speed, and self-awareness, not storytelling polish. Interviewers want to understand how you respond when outcomes are uncertain and feedback is immediate.

Discussion areas typically include:

• A time you made a decision with incomplete information
• How you responded when a result was worse than expected
• How you adjusted your approach after receiving new data
• How you manage stress in fast-paced environments

Strong candidates are precise about:

• What assumptions they made
• What signals indicated those assumptions were wrong
• How they corrected course

Answers that frame mistakes as abstract learning moments without concrete adjustment tend to underperform.

Optiver signal: Accountability paired with rapid learning.

Final review and offer

After interviews conclude, feedback is consolidated across interviewers. Optiver evaluates candidates against a high and consistent bar, particularly on reasoning quality, adaptability, and performance under pressure.

Final decisions consider:

• Consistency of logic across rounds
• Ability to adapt when constraints change
• Communication clarity under stress
• Team and role fit

If aligned, Optiver extends an offer reflecting role scope, location, and expected impact. Compensation discussions are direct and performance-oriented.

This stage is also your opportunity to clarify:

• Expectations around ramp-up and performance
• Feedback and evaluation cadence
• Growth trajectory within the firm

Tip: Ask concrete, role-specific questions. Generic questions can signal misalignment with Optiver’s culture.

What distinguishes strong Optiver candidates in the process

Across the full interview loop, candidates who perform best consistently demonstrate:

• Probabilistic thinking rather than deterministic answers
• Comfort adjusting reasoning in real time
• Discipline under time pressure
• Clear communication of assumptions and trade-offs

Optiver interviews are not about memorized tricks. They are about whether your thinking can keep up with fast, competitive markets.

Optiver interviews are designed to test probabilistic reasoning, correctness under time pressure, and disciplined decision-making. Questions are rarely framed as open-ended brainstorming. Instead, interviewers push candidates to reason step by step, quantify uncertainty, and adjust logic quickly when assumptions change.

Even when questions look familiar, Optiver interviewers probe aggressively on expected value, distributions, edge cases, and decision thresholds. Speed matters, but only when paired with correctness.

For role-specific calibration, use the dedicated guides below:

• Optiver Software Engineer
• Optiver Data Engineer
• Optiver Machine Learning Engineer
• Optiver Data Scientist
• Optiver Research Scientist
• Optiver Data Analyst
• Optiver Product Manager

Probability, mental math, and expected value questions

Best paired with: Optiver Research Scientist, Optiver Data Scientist, Optiver Machine Learning Engineer

Probability and expected value questions are central to Optiver interviews. Interviewers care about how you structure uncertainty, not just the final number.

Sample Optiver-style probability questions

Optiver signal: Correct probabilistic structure beats fast arithmetic.

Logic and quantitative reasoning questions

Best paired with: Optiver Research Scientist, Optiver Data Analyst, Optiver Business-facing roles

These questions test whether you can reason clearly under constraints and adjust logic quickly.

Sample Optiver-style logic questions

Optiver signal: Structure before calculation.

Coding and algorithmic questions

Best paired with: Optiver Software Engineer, Optiver Data Engineer

Coding questions emphasize correctness, edge cases, and clarity, often under time pressure. Interviewers interrupt to test whether you notice flaws.

Sample Optiver-style coding questions

Optiver signal: Defensive correctness beats clever shortcuts.

Data analysis and SQL questions

Best paired with: Optiver Data Analyst, Optiver Data Engineer

SQL and data questions test precision, validation, and metric correctness. Interviewers care deeply about grain, joins, and silent errors.

Sample Optiver-style SQL questions

Optiver signal: Silent assumptions are treated as mistakes.

Machine learning and modeling questions

Best paired with: Optiver Machine Learning Engineer, Optiver Data Scientist, Optiver Research Scientist

ML interviews focus on evaluation, robustness, and decision impact, not algorithm novelty.

Sample Optiver-style ML questions

Optiver signal: Models must be defensible in real time.

Behavioral and judgment questions

Best paired with all Optiver roles.

Behavioral interviews at Optiver focus on decision quality, learning speed, and stress management, not storytelling polish.

Common Optiver behavioral prompts

• Tell me about a time you made a decision under uncertainty
• Describe a mistake you caught late
• How do you react when results contradict expectations?
• How do you stay calm under time pressure?
• How do you incorporate feedback quickly?

Interviewers listen for what changed in your thinking, not just outcomes.

To pressure-test delivery, rehearse with the AI interview tool or simulate full rounds using mock interviews

Preparing for Optiver interviews is about training speed with correctness, not memorization. Optiver interviewers want to see whether you can make good decisions quickly while maintaining disciplined reasoning.

Strong candidates prepare differently than they would for traditional tech or consulting interviews.

Train probabilistic thinking daily

Optiver interviews are fundamentally probabilistic. You should be comfortable reasoning in terms of expected value, distributions, and uncertainty, even when problems are presented verbally or under time pressure.

Effective preparation includes:

• Practicing expected value calculations out loud
• Translating word problems into probability models
• Updating conclusions when assumptions change
• Estimating outcomes without overfitting precision

Avoid framing answers as certainties. Interviewers expect uncertainty to be explicit.

Optiver signal: Comfort with uncertainty beats deterministic confidence.

Balance speed and discipline

Unlike firms that prioritize only correctness, Optiver also values controlled speed. Interviewers observe how quickly you can arrive at a correct structure without panicking or cutting logical corners.

Practice by:

• Timing mental math exercises
• Solving problems with limited writing
• Explaining reasoning while calculating
• Recovering calmly when corrected

Rushing without structure is penalized more than slowing down to maintain correctness.

Optiver signal: Fast and structured beats fast and messy.

Practice reasoning out loud under pressure

Optiver interviewers care deeply about your reasoning process. Silence or internal thinking without explanation can be interpreted as risk.

Strong candidates:

• Restate the problem clearly
• Outline an approach before solving
• Call out assumptions immediately
• Narrate updates when constraints change

Use the Interview Query question bank to practice structured explanations across probability, logic, coding, and data problems.

Prepare feedback-driven stories

Behavioral interviews at Optiver focus on learning speed and adaptability. You should prepare concrete examples where you can explain:

• A decision made under uncertainty
• What feedback or data changed your view
• How quickly you adjusted your approach
• What improved as a result

Avoid overly polished stories. Interviewers want realism and clarity.

Practice delivery with the AI interview tool to remove vagueness and filler.

Align preparation to your role

While Optiver’s bar is consistent, emphasis varies by role:

• Engineering roles: correctness, edge cases, low-latency thinking
• Data roles: SQL rigor, metric precision, validation
• Research and ML roles: probability, modeling assumptions, robustness
• Product roles: decision trade-offs, prioritization under constraints

Generic preparation is rarely sufficient at Optiver.

To simulate real interview pressure, use mock interviews.

Optiver is known for offering highly competitive compensation, particularly for quantitative, engineering, and research roles. Total compensation typically includes base salary and performance-based bonuses tied to individual and firm performance.

The ranges below reflect aggregated self-reported data from Levels.fyi and are directional benchmarks rather than guarantees.

Average Compensation by Role (United States)

How Optiver compensation works

Optiver compensation reflects:

• Individual performance and learning speed
• Role criticality to trading outcomes
• Seniority and scope of ownership
• Geography and desk assignment

Unlike many tech companies, compensation is less predictable year to year and more tightly linked to performance.

You can benchmark Optiver pay against other firms using the Interview Query companies directory.

How difficult are Optiver interviews?

Optiver interviews are highly challenging. They test probabilistic reasoning, decision-making under pressure, and disciplined thinking. Many strong candidates struggle not because of lack of intelligence, but because they rush or fail to explain assumptions clearly.

What should I expect in an Optiver interview?

You can expect probability and logic questions, mental math, coding or data problems depending on the role, and behavioral interviews focused on learning speed and adaptability. Interviewers frequently adjust assumptions mid-problem to test flexibility.

Does Optiver care more about speed or correctness?

Optiver values both, but correctness comes first. Speed is only rewarded when paired with clean reasoning and valid assumptions.

Are Optiver interviews role-specific?

Yes. While the firm’s evaluation philosophy is consistent, depth expectations differ across engineering, data, research, and product roles. Preparing with the role-specific guides significantly improves performance.

How can I improve my chances at Optiver?

Strong candidates focus on:

1. Probabilistic thinking
2. Clear reasoning under pressure
3. Explicit assumptions
4. Fast learning from feedback

Deliberate practice with realistic problems and live interview simulation makes a measurable difference.

Interviews at Optiver reflect how the firm operates in real markets: fast, uncertain, and unforgiving of mistakes. The goal is not to impress interviewers with clever tricks, but to demonstrate that your decisions remain sound when conditions change quickly.

If you want to prepare in a way that matches Optiver’s expectations:

• Practice probability, logic, and coding in the Interview Query question bank
• Improve structured thinking with the AI interview tool
• Simulate real interview pressure using mock interviews
• Build fundamentals with curated learning paths

At Optiver, the edge comes from thinking clearly when time is scarce.