Akuna Capital Machine Learning Engineer Interview Questions + Guide in 2025

Akuna Capital is a technology-driven trading firm specializing in derivatives and providing liquidity in various markets through quantitative strategies and innovative technology solutions.

The Machine Learning Engineer role at Akuna Capital involves designing, implementing, and optimizing machine learning algorithms to enhance trading strategies and improve decision-making processes. Key responsibilities include developing predictive models, analyzing large datasets to extract actionable insights, and collaborating with quantitative researchers and software engineers to integrate machine learning solutions into trading systems. Ideal candidates will possess a strong background in statistics, data analysis, and programming languages such as Python or C++. Additionally, familiarity with financial markets and a keen understanding of algorithmic trading will set a candidate apart. Traits like problem-solving skills, adaptability to fast-paced environments, and effective communication abilities are essential for success in this role.

This guide will help you prepare for your interview by providing insights into the expectations and skills needed for the Machine Learning Engineer position at Akuna Capital, allowing you to showcase your qualifications effectively.

The interview process for a Machine Learning Engineer at Akuna Capital is structured to assess both technical skills and cultural fit. It typically consists of several stages, each designed to evaluate different competencies relevant to the role.

1. Online Assessment

The first step in the interview process is an online assessment, usually conducted through HackerRank. Candidates are given a set time, often around 60 to 120 minutes, to complete multiple coding questions that range from easy to medium difficulty. These questions often focus on data structures, algorithms, and basic programming concepts. Expect to encounter a mix of multiple-choice questions and coding challenges that test your problem-solving abilities and understanding of fundamental programming principles.

2. Technical Phone Interview

Following the online assessment, candidates who perform well are typically invited to a technical phone interview. This interview usually lasts around 30 to 60 minutes and is conducted via a video conferencing platform like Zoom. During this session, candidates are expected to solve coding problems in real-time, often using a shared coding environment. The interviewer may ask questions that require you to explain your thought process, discuss time and space complexity, and demonstrate your coding skills in a programming language of your choice.

3. Onsite Interviews

Candidates who successfully navigate the phone interview stage may be invited for onsite interviews, which can consist of multiple rounds. These rounds often include a mix of technical and behavioral interviews. Technical rounds may involve solving complex coding problems, discussing machine learning concepts, and demonstrating your understanding of algorithms and data structures. Behavioral interviews will focus on your past experiences, teamwork, and how you align with Akuna Capital's culture. Be prepared to discuss your resume in detail and answer questions about your previous projects and roles.

4. Final Interview

The final stage may include a wrap-up interview with HR or senior management. This interview often focuses on assessing your fit within the company culture and discussing your long-term career goals. Expect questions about your motivations for joining Akuna Capital and how you envision contributing to the team.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage of the process.

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

Understand the Technical Landscape

As a Machine Learning Engineer at Akuna Capital, you will be expected to have a solid grasp of algorithms, data structures, and machine learning concepts. Familiarize yourself with the latest trends in machine learning, including supervised and unsupervised learning, neural networks, and natural language processing. Additionally, brush up on your coding skills in languages like Python and C++, as these are commonly used in the role. Make sure to practice coding problems on platforms like LeetCode, focusing on medium to hard difficulty levels, as many candidates report facing challenging questions during the interview process.

Prepare for Coding Assessments

Expect to encounter multiple coding assessments, often conducted through platforms like HackerRank. These assessments typically include a mix of multiple-choice questions and coding challenges. Focus on fundamental concepts such as string manipulation, array operations, and graph algorithms. Given the feedback from previous candidates, it’s crucial to practice problems that involve dynamic programming and data structures, as these topics frequently appear in the assessments. Remember to manage your time effectively during these tests, as you may have limited time to complete multiple questions.

Be Ready for Behavioral Questions

While technical skills are paramount, Akuna Capital also values cultural fit and interpersonal skills. Prepare for behavioral questions that explore your past experiences, teamwork, and problem-solving abilities. Reflect on your previous projects and be ready to discuss your contributions, challenges faced, and how you overcame them. Demonstrating a collaborative mindset and a willingness to learn will resonate well with interviewers.

Engage with Your Interviewers

During the interview, especially in technical discussions, don’t hesitate to ask clarifying questions if you don’t fully understand a problem. Interviewers appreciate candidates who communicate openly and seek to clarify expectations. Additionally, be prepared to explain your thought process as you work through coding problems. This not only showcases your problem-solving skills but also allows interviewers to gauge your approach to challenges.

Know Your Resume Inside and Out

Be prepared to discuss your resume in detail, including your technical projects and any relevant coursework. Interviewers may ask you to elaborate on specific technologies or methodologies you’ve used, so ensure you can speak confidently about your experiences. Highlight any machine learning projects you’ve worked on, as this will demonstrate your practical knowledge in the field.

Stay Calm and Positive

Interviews can be stressful, but maintaining a calm and positive demeanor can make a significant difference. If you encounter a challenging question, take a moment to think it through rather than rushing to an answer. If you find yourself stuck, it’s okay to acknowledge it and discuss your thought process with the interviewer. This shows your ability to handle pressure and your willingness to learn from the experience.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Akuna Capital. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer position at Akuna Capital. The interview process will likely focus on your coding skills, understanding of algorithms, data structures, and your ability to apply machine learning concepts to real-world problems. Be prepared to demonstrate your technical knowledge and problem-solving abilities through coding challenges and theoretical questions.

Coding and Algorithms

1. Can you write a function to determine if a number has an even or odd number of 1s in its binary representation?

This question tests your understanding of bit manipulation and binary representation.

How to Answer

Explain your approach to converting the number to binary and counting the 1s. Discuss the time complexity of your solution.

Example

“I would convert the number to binary and iterate through each bit, counting the number of 1s. If the count is even, I return true; otherwise, I return false. This approach runs in O(log n) time, where n is the number.”

2. How would you implement a function to check if two nodes in a graph are connected?

This question assesses your knowledge of graph theory and traversal algorithms.

How to Answer

Discuss using depth-first search (DFS) or breadth-first search (BFS) to explore the graph and check for connectivity.

Example

“I would use a DFS approach, starting from one node and marking visited nodes. If I reach the second node during traversal, they are connected. This method has a time complexity of O(V + E), where V is the number of vertices and E is the number of edges.”

3. Describe how you would implement a maximum subarray problem.

This question evaluates your understanding of dynamic programming.

How to Answer

Explain the Kadane’s algorithm and how it efficiently finds the maximum sum of a contiguous subarray.

Example

“I would use Kadane’s algorithm, which maintains a running sum and resets it when it becomes negative. This runs in O(n) time, making it efficient for large arrays.”

4. Can you explain how to detect front-running in a trading system?

This question tests your ability to apply algorithms to financial scenarios.

How to Answer

Discuss the logic behind detecting front-running and the data structures you would use.

Example

“I would analyze the order book data and look for patterns where a trader places an order just before a large market order. I would use a sliding window approach to track recent trades and identify suspicious activity.”

5. How would you implement a class to manage an order book?

This question assesses your object-oriented design skills and understanding of data structures.

How to Answer

Outline the key components of an order book and how you would structure the class.

Example

“I would create an OrderBook class with methods to add, remove, and match orders. I would use a priority queue to manage orders based on price and time, ensuring efficient retrieval and processing.”

Machine Learning Concepts

1. Explain the difference between supervised and unsupervised learning.

This question tests your foundational knowledge of machine learning.

How to Answer

Define both terms and provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on labeled data, such as regression and classification algorithms. Unsupervised learning, on the other hand, deals with unlabeled data, using clustering and dimensionality reduction techniques.”

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

This question evaluates your understanding of model performance and generalization.

How to Answer

Discuss the concept of overfitting and techniques to mitigate it.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. It can be prevented by using techniques like cross-validation, regularization, and pruning in decision trees.”

3. Can you explain the bias-variance tradeoff?

This question assesses your understanding of model evaluation.

How to Answer

Define bias and variance, and explain how they affect model performance.

Example

“The bias-variance tradeoff refers to the balance between a model’s ability to minimize bias (error due to overly simplistic assumptions) and variance (error due to excessive complexity). A good model should find a balance to minimize total error.”

4. Describe a machine learning project you have worked on. What challenges did you face?

This question allows you to showcase your practical experience.

How to Answer

Discuss the project, your role, and specific challenges you encountered.

Example

“I worked on a project to predict stock prices using historical data. One challenge was dealing with missing values, which I addressed by implementing imputation techniques. The project improved our prediction accuracy by 15%.”

5. How do you evaluate the performance of a machine learning model?

This question tests your knowledge of model evaluation metrics.

How to Answer

Discuss various metrics and when to use them.

Example

“I evaluate model performance using metrics like accuracy, precision, recall, and F1-score for classification tasks, and mean squared error for regression tasks. The choice of metric depends on the specific problem and business objectives.”