Susquehanna International Group, LLP Machine Learning Engineer Interview Questions + Guide in 2025

Susquehanna International Group, LLP (SIG) is a global trading and technology firm that leverages quantitative research and advanced technology to drive its trading strategies.

As a Machine Learning Engineer at SIG, you will play a crucial role in developing and implementing machine learning algorithms that enhance trading strategies and improve operational efficiencies. Your key responsibilities will include designing and optimizing machine learning models, conducting rigorous data analysis to extract insights from large datasets, and collaborating with cross-functional teams to integrate ML solutions into the trading systems. A strong understanding of algorithms, data structures, and statistical methods is essential, alongside proficiency in programming languages such as Python, Java, or C++. You should also possess excellent problem-solving skills, the ability to work under pressure, and a passion for leveraging technology to make data-driven decisions. SIG values innovation, teamwork, and a commitment to excellence, making it imperative for the ideal candidate to align with these core principles.

This guide will equip you with tailored insights and strategies to ace your interview for the Machine Learning Engineer role at SIG, enhancing your confidence and preparedness.

The interview process for a Machine Learning Engineer at Susquehanna International Group is structured and thorough, designed to assess both technical skills and cultural fit. The process typically unfolds in several key stages:

1. Initial Phone Screen

The first step usually involves a phone interview with a recruiter. This conversation is focused on understanding your background, motivations for applying, and general fit for the company culture. Expect to discuss your previous experiences, technical skills, and why you are interested in the role at SIG.

2. Online Assessment

Following the initial screen, candidates are often required to complete an online coding assessment, typically hosted on platforms like CodeSignal or Codility. This assessment usually consists of multiple coding problems that test your problem-solving abilities and knowledge of algorithms and data structures. The questions can range from easy to medium difficulty, and it is advisable to practice similar problems in advance to ensure you are well-prepared.

3. Technical Interview

If you perform well on the online assessment, the next step is a technical interview, which may be conducted over the phone or via video call. During this interview, you will engage with an engineer who will ask you to explain your solutions to the coding problems from the assessment. You may also be asked additional technical questions that delve into your understanding of machine learning concepts, programming languages, and system design.

4. Onsite Interviews

Candidates who successfully navigate the technical interview are typically invited for onsite interviews, which can last several hours and consist of multiple rounds. These rounds often include hands-on coding exercises, system design challenges, and discussions about your past projects. Interviewers will assess your ability to write clean, efficient code and your understanding of machine learning principles. Expect to collaborate with interviewers on coding problems, as this is often a cooperative process rather than a strictly evaluative one.

5. Behavioral and Cultural Fit Interview

The final round usually includes a behavioral interview, where you will discuss your work style, team dynamics, and how you align with SIG's values. This is an opportunity for you to demonstrate your interpersonal skills and how you would contribute to the company culture.

As you prepare for your interview, it’s essential to be ready for a variety of technical and behavioral questions that reflect the skills and experiences relevant to the Machine Learning Engineer role.

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

Prepare for Coding Assessments

Expect to face a coding assessment early in the interview process, often through platforms like CodeSignal or HackerRank. The questions typically range from easy to medium difficulty, focusing on data structures and algorithms. To prepare, practice solving problems on LeetCode, especially those that involve arrays, hashmaps, and basic algorithms. Familiarize yourself with common coding patterns and ensure you can write clean, efficient code under time constraints.

Master Data Structures and Algorithms

A strong understanding of data structures (like linked lists, trees, and hash tables) and algorithms (such as sorting and searching) is crucial. Be prepared to discuss the time and space complexity of your solutions. Interviewers often ask about the trade-offs between different data structures, so be ready to explain your choices and the reasoning behind them. Practicing with mock interviews can help you articulate your thought process clearly.

Emphasize Problem-Solving Skills

During technical interviews, you may encounter open-ended questions that require you to design systems or solve complex problems. Approach these questions methodically: clarify requirements, outline your thought process, and discuss potential solutions. Interviewers appreciate candidates who can think critically and communicate their reasoning effectively. Be prepared to iterate on your solutions based on feedback from the interviewers.

Showcase Your Projects and Experience

Be ready to discuss your past projects in detail, particularly those that relate to machine learning and software development. Highlight your specific contributions, the technologies you used, and the challenges you faced. This not only demonstrates your technical skills but also your ability to work collaboratively and adapt to new situations. Tailor your examples to align with SIG's focus on innovation and technology.

Understand the Company Culture

SIG values a collaborative and supportive work environment. During your interviews, express your enthusiasm for teamwork and your willingness to learn from others. Be prepared to discuss how you handle feedback and work in a team setting. Additionally, research SIG's recent projects or initiatives to show your genuine interest in the company and its mission.

Prepare for Behavioral Questions

Expect behavioral questions that assess your fit within the company culture. Prepare to discuss your motivations, work ethic, and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing clear examples that illustrate your skills and experiences. This will help you convey your personality and values effectively.

Follow Up Professionally

After your interviews, send a thank-you email to your interviewers expressing your appreciation for the opportunity to interview. This not only shows professionalism but also reinforces your interest in the position. If you don’t hear back within a reasonable timeframe, consider following up politely to inquire about your application status.

By focusing on these areas, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great cultural fit for Susquehanna International Group. Good luck!

Coding and Algorithms

1. Describe your approach to solving a coding problem. Can you walk us through a specific example?

This question assesses your problem-solving process and ability to communicate your thought process clearly.

How to Answer

Explain your approach step-by-step, including how you break down the problem, the algorithms you consider, and how you implement your solution.

Example

“I start by understanding the problem requirements and constraints. For instance, when tasked with finding the K’th largest element in an array, I first consider sorting the array, but then I realize that using a min-heap would be more efficient. I implement the heap and ensure to handle edge cases, such as when K is larger than the array size.”

2. How would you implement a function to validate a Sudoku puzzle?

This question tests your understanding of data structures and algorithms, particularly in the context of a common problem.

How to Answer

Discuss the data structures you would use, such as arrays or sets, and outline your approach to checking the validity of the Sudoku rules.

Example

“I would use a set to track the numbers seen in each row, column, and 3x3 subgrid. As I iterate through the board, I would check if a number has already been seen in any of these sets. If it has, the puzzle is invalid; otherwise, I add it to the respective sets.”

3. Can you explain how hash tables work and their time complexity?

This question evaluates your knowledge of fundamental data structures and their performance characteristics.

How to Answer

Provide a brief overview of hash tables, including how they store key-value pairs and the average time complexity for operations.

Example

“Hash tables use a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. The average time complexity for insertions, deletions, and lookups is O(1), but in the worst case, it can degrade to O(n) if many collisions occur.”

4. What is the difference between a linked list and an array? When would you choose one over the other?

This question assesses your understanding of data structures and their use cases.

How to Answer

Discuss the characteristics of both data structures, including their memory usage and performance for various operations.

Example

“Arrays provide O(1) access time but have a fixed size, while linked lists allow for dynamic sizing but have O(n) access time. I would choose a linked list when I need frequent insertions and deletions, while an array is preferable for scenarios requiring fast access to elements.”

5. How would you design a system to manage a grocery store's inventory?

This question tests your system design skills and ability to think through a real-world application.

How to Answer

Outline the components of the system, including data storage, user interfaces, and how you would handle transactions.

Example

“I would start by defining the core entities: products, categories, and transactions. I would use a relational database to store product information and implement a RESTful API for inventory management. The system would allow for CRUD operations on products and track stock levels in real-time.”

Machine Learning Concepts

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

This question evaluates your foundational knowledge of machine learning paradigms.

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 using linear regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior using K-means.”

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

This question assesses your understanding of model performance and generalization.

How to Answer

Discuss the concept of overfitting and various techniques to mitigate it.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I would use techniques such as cross-validation, regularization, and pruning in decision trees.”

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

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Detail the project, your role, and specific challenges you encountered, along with how you addressed them.

Example

“I worked on a project to predict customer churn. One challenge was dealing with imbalanced classes. I addressed this by using techniques like SMOTE for oversampling the minority class and adjusting the classification threshold to improve recall.”

4. 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 based on the problem type.

Example

“I evaluate model performance using metrics like accuracy, precision, recall, and F1-score for classification tasks. For regression, I use mean squared error and R-squared. The choice of metric depends on the specific business objectives and the cost of false positives versus false negatives.”

5. What is the purpose of feature engineering in machine learning?

This question assesses your understanding of the importance of data preparation.

How to Answer

Explain the concept of feature engineering and its impact on model performance.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. It can significantly enhance the model's ability to learn patterns, as seen in my experience where I derived interaction terms from categorical variables to boost predictive accuracy.”