Optiver Machine Learning Engineer Interview Questions + Guide in 2025

Optiver is a leading global market maker, leveraging technology and quantitative research to provide liquidity in financial markets around the world.

As a Machine Learning Engineer at Optiver, you will be responsible for developing and implementing sophisticated machine learning models and algorithms to optimize trading strategies and improve operational efficiency. Key responsibilities include collaborating with quantitative researchers to identify data-driven insights, designing robust machine learning pipelines, and conducting experiments to assess model performance. You will also be tasked with integrating machine learning systems into existing trading infrastructure, ensuring scalability and reliability.

To excel in this role, you should possess strong programming skills in languages such as Python or C++, deep knowledge of machine learning frameworks, and experience in statistical analysis. Additionally, a background in finance or quantitative analysis is highly desirable, as understanding market dynamics is crucial for effectively applying machine learning techniques in trading environments. The ideal candidate will demonstrate a passion for problem-solving, a keen analytical mindset, and the ability to work collaboratively in a fast-paced, high-pressure environment.

This guide is designed to help you prepare for your interview at Optiver by providing insights into the role and the types of questions you may encounter, ultimately giving you the confidence to showcase your skills and fit for the position.

The interview process for a Machine Learning Engineer at Optiver is designed to rigorously assess both technical skills and cultural fit. It typically unfolds in several stages, each aimed at evaluating different competencies essential for the role.

1. Online Assessment

The first step in the interview process is an online assessment that can last between 2 to 3 hours. This assessment usually consists of multiple components, including coding challenges, logic puzzles, and cognitive tests. Candidates may encounter HackerRank-style questions that test their programming skills, as well as brain games that assess numerical reasoning and problem-solving abilities. The goal of this stage is to gauge the candidate's foundational skills in programming and mathematics, which are critical for a Machine Learning Engineer.

2. Behavioral Interview

Following the online assessment, candidates typically participate in a behavioral interview with a recruiter or HR representative. This interview focuses on understanding the candidate's motivations for applying to Optiver, their past experiences, and how they align with the company's culture. Expect questions that explore your teamwork, conflict resolution, and project management skills. This stage is crucial for determining if the candidate's values and work style fit well within Optiver's collaborative environment.

3. Technical Interview

Candidates who successfully pass the behavioral interview will move on to a technical interview. This round often involves discussions around machine learning concepts, algorithms, and system design. Candidates may be asked to solve coding problems in real-time, demonstrating their thought process and problem-solving approach. Additionally, expect questions that require you to explain your past projects, particularly those that involve machine learning applications, and how you approached challenges within those projects.

4. Final Round Interviews

The final stage of the interview process typically consists of multiple interviews, which may include both technical and behavioral assessments. Candidates might engage with various team members, including senior engineers and managers, to discuss technical challenges, design systems, and solve complex problems collaboratively. This stage is designed to assess not only technical proficiency but also how well candidates can communicate their ideas and work within a team setting.

Throughout the interview process, candidates should be prepared for a mix of technical challenges, behavioral questions, and discussions about their experiences and motivations.

Now, let's delve into the specific interview questions that candidates have encountered during the process.

Here are some tips to help you excel in your interview.

Understand the Assessment Structure

The interview process at Optiver typically involves multiple stages, starting with an online assessment that tests your coding skills, logical reasoning, and quantitative abilities. Familiarize yourself with the structure of these assessments, as they often include coding questions, multiple-choice questions, and even cognitive games. Practicing similar formats will help you manage your time effectively during the actual assessment.

Prepare for Technical and Behavioral Questions

Expect a mix of technical and behavioral questions throughout the interview process. Be ready to discuss your past projects in detail, especially those that showcase your machine learning skills. When discussing your projects, be clear about your role, the challenges you faced, and the impact of your work. Additionally, prepare for behavioral questions that assess your motivations and fit within the company culture, such as why you want to work at Optiver and how you handle stress.

Hone Your Problem-Solving Skills

Optiver places a strong emphasis on problem-solving abilities, particularly in high-pressure situations. Practice solving algorithmic problems and Fermi questions, as these are common in interviews. Focus on articulating your thought process clearly while solving problems, as interviewers are interested in how you approach challenges rather than just the final answer.

Embrace the Company Culture

Optiver values a collaborative and innovative environment. During your interviews, demonstrate your ability to work well in teams and your enthusiasm for contributing to a dynamic workplace. Be prepared to discuss how you handle conflicts and collaborate with others, as these traits are essential for success in their culture.

Be Ready for Rigorous Assessments

The interview process can be intense, with some candidates reporting long assessments and challenging questions. Approach the assessments with a calm mindset, and practice under timed conditions to build your stamina. Remember that it's okay to take a moment to think through your answers, especially during live coding rounds.

Communicate Effectively

Throughout the interview, maintain open and clear communication with your interviewers. If you encounter a challenging question, verbalize your thought process and reasoning. This not only shows your analytical skills but also allows interviewers to understand your approach better. They appreciate candidates who can articulate their thoughts, even if they don't arrive at the correct answer immediately.

Follow Up with Questions

At the end of your interviews, take the opportunity to ask insightful questions about the team, projects, and company culture. This demonstrates your genuine interest in the role and helps you assess if Optiver is the right fit for you. Tailor your questions based on your research about the company and the specific team you are interviewing for.

By following these tips and preparing thoroughly, you can enhance your chances of success in the interview process at Optiver. Good luck!

Technical Skills

1. Describe a machine learning project you worked on. What were the challenges, and how did you overcome them?

This question assesses your practical experience with machine learning and your problem-solving skills.

How to Answer

Focus on a specific project, detailing the problem you aimed to solve, the methods you used, and the results. Highlight any obstacles you faced and how you addressed them.

Example

“In my last project, I developed a predictive model for stock price movements using historical data. One challenge was dealing with missing data, which I addressed by implementing interpolation techniques. The model ultimately improved our prediction accuracy by 15%.”

2. How do you approach feature selection in a machine learning model?

This question evaluates your understanding of model optimization and data preprocessing.

How to Answer

Discuss the techniques you use for feature selection, such as correlation analysis, recursive feature elimination, or using algorithms like LASSO. Mention how these techniques help improve model performance.

Example

“I typically start with correlation analysis to identify features that have a strong relationship with the target variable. Then, I use recursive feature elimination to iteratively remove less important features, which helps in reducing overfitting and improving model interpretability.”

3. Can you explain the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each. This shows your understanding of the broader machine learning landscape.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

4. What is overfitting, and how can it be prevented?

This question assesses your understanding of model performance and generalization.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent this, I use techniques like cross-validation to ensure the model generalizes well and apply regularization methods to penalize overly complex models.”

5. Describe a time when you had to optimize a machine learning model. What steps did you take?

This question evaluates your practical experience in model optimization.

How to Answer

Detail the specific model you optimized, the metrics you focused on, and the techniques you employed to achieve better performance.

Example

“I worked on optimizing a recommendation system where the initial model had a low precision score. I analyzed the feature importance and decided to incorporate additional user behavior data. After retraining the model, I achieved a 20% increase in precision.”

Statistics & Probability

1. Explain the Central Limit Theorem and its significance in statistics.

This question tests your understanding of fundamental statistical concepts.

How to Answer

Define the Central Limit Theorem and explain its importance in making inferences about population parameters.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is significant because it allows us to make inferences about population parameters using sample data, which is crucial in hypothesis testing.”

2. How do you handle missing data in a dataset?

This question assesses your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use mean or median imputation for numerical data or mode for categorical data. If the missing data is substantial, I may consider using algorithms that can handle missing values directly.”

3. What is the difference between Type I and Type II errors?

This question evaluates your understanding of hypothesis testing.

How to Answer

Define both types of errors and provide examples to illustrate the differences.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a medical trial, a Type I error could mean concluding a drug is effective when it is not, while a Type II error would mean missing the opportunity to identify an effective drug.”

4. Can you explain what p-values represent?

This question tests your knowledge of statistical significance.

How to Answer

Define p-values and explain their role in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that the observed data is unlikely under the null hypothesis, leading us to consider rejecting it in favor of the alternative hypothesis.”

5. Describe a situation where you used statistical analysis to solve a problem.

This question assesses your practical application of statistical methods.

How to Answer

Provide a specific example where you applied statistical analysis to derive insights or make decisions.

Example

“In a project analyzing customer churn, I used logistic regression to identify key factors influencing churn rates. By analyzing the coefficients, I discovered that customer support response time was a significant predictor, leading to targeted improvements in that area and a subsequent reduction in churn by 10%.”

Behavioral Questions

1. Why do you want to work at Optiver?

This question assesses your motivation and fit for the company.

How to Answer

Discuss your interest in the company’s mission, culture, and how your skills align with their goals.

Example

“I admire Optiver’s commitment to innovation in trading technology and its collaborative culture. I believe my background in machine learning and passion for data-driven decision-making align perfectly with Optiver’s mission to enhance market efficiency.”

2. Describe a time when you faced a significant challenge in a project. How did you handle it?

This question evaluates your problem-solving and resilience.

How to Answer

Detail a specific challenge, your approach to overcoming it, and the outcome.

Example

“During a project, we faced unexpected data quality issues that threatened our timeline. I organized a team meeting to brainstorm solutions, and we decided to implement a data cleaning pipeline. This not only resolved the issue but also improved our data processing efficiency for future projects.”

3. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use.

Example

“I prioritize tasks based on their deadlines and impact on project goals. I use project management tools like Trello to visualize my workload and ensure I allocate time effectively. Regular check-ins with my team also help me stay aligned with our objectives.”

4. Tell me about a time you worked in a team. What role did you play?

This question evaluates your teamwork and collaboration skills.

How to Answer

Describe your role in the team, how you contributed, and the outcome of the collaboration.

Example

“In a recent project, I took on the role of the data analyst, collaborating with developers and product managers. I facilitated communication between the team members, ensuring everyone was aligned on our goals. Our collaborative effort led to a successful product launch that exceeded user engagement expectations.”

5. How do you handle feedback and criticism?

This question assesses your ability to accept and learn from feedback.

How to Answer

Discuss your approach to receiving feedback and how you use it for personal and professional growth.

Example

“I view feedback as an opportunity for growth. When I receive criticism, I take time to reflect on it and identify actionable steps for improvement. For instance, after receiving feedback on my presentation skills, I enrolled in a public speaking course, which significantly enhanced my ability to communicate complex ideas effectively.”