Relativity Machine Learning Engineer Interview Questions + Guide in 2025

Relativity is a leading software company based in Chicago, specializing in data management and machine learning solutions that empower organizations to harness the power of their data effectively.

As a Machine Learning Engineer at Relativity, you will play a critical role in collaborating with data scientists and product teams within the AI group to deploy machine learning solutions at scale. Your key responsibilities will include participating in all phases of the service lifecycle—from conception through development, deployment, and operation—while contributing to and enhancing existing automated continuous integration and continuous deployment (CICD) workflows. You will engage closely with data scientists, product managers, and engineering teams to transform machine learning feature ideas into proof of concepts and actionable project plans.

To excel in this role, you should have a strong foundation in algorithms and programming, with a significant emphasis on Python as your primary coding language, as well as a solid understanding of machine learning principles and practices. Experience with deep learning techniques, data preparation, model evaluation, and error analysis will be highly valued. Furthermore, your ability to communicate effectively and collaborate with cross-functional teams, along with a proactive approach to mitigating risks and issues, will enable you to thrive in this dynamic environment.

This guide aims to equip you with the necessary insights and preparation strategies to tackle your interview confidently, ensuring you can effectively demonstrate your technical skills and alignment with Relativity's core values.

The interview process for a Machine Learning Engineer at Relativity is structured and involves multiple stages to assess both technical and cultural fit.

1. Initial Screening

The process begins with a 30-minute phone interview with a recruiter. This initial screening focuses on your background, experience, and motivation for applying to Relativity. The recruiter will also gauge your fit within the company culture and provide an overview of the interview process.

2. Technical Assessment

Following the initial screening, candidates are required to complete a technical assessment, often conducted through a platform like HackerRank. This assessment typically includes coding challenges that test your problem-solving skills, knowledge of data structures, and algorithms. Expect questions that require you to demonstrate your understanding of machine learning concepts and programming proficiency, particularly in Python.

3. Technical Interviews

Candidates who pass the technical assessment will move on to a series of technical interviews. These interviews usually consist of two to three rounds, where you will engage with engineers and data scientists. The focus will be on your ability to apply machine learning techniques, discuss algorithms, and solve coding problems in real-time. You may also be asked to explain your thought process and approach to various technical challenges, including system design and model evaluation.

4. Panel Interview

If you successfully navigate the technical interviews, you will participate in a panel interview. This session typically involves multiple interviewers from different teams, including product managers and senior engineers. The panel will assess your technical skills, collaborative abilities, and how well you align with Relativity's core values. Expect questions that explore your past experiences, project management skills, and how you handle challenges in a team setting.

5. Final Interview

The final stage of the interview process may include a presentation or discussion with senior leadership, including the hiring manager and director. This is an opportunity for you to showcase your understanding of machine learning applications and how you can contribute to the team. You may be asked to present a project or a proof of concept that demonstrates your skills and thought process.

Throughout the interview process, be prepared for a mix of behavioral and technical questions that assess both your expertise in machine learning and your fit within the company culture.

Next, let's delve into the specific interview questions that candidates have encountered during their interviews at Relativity.

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

Understand the Interview Structure

The interview process at Relativity typically consists of multiple rounds, including an initial HR screening, a technical coding assessment, and several interviews with team members. Be prepared for a mix of behavioral and technical questions, as well as a panel interview that may include coding challenges and system design discussions. Familiarize yourself with the format and structure of each round to manage your time and expectations effectively.

Master the Technical Skills

Given the emphasis on algorithms and Python in the role, ensure you are well-versed in these areas. Brush up on your knowledge of data structures, algorithms, and machine learning concepts. Practice coding problems on platforms like LeetCode or HackerRank, focusing on algorithmic thinking and problem-solving. Be ready to discuss your thought process and approach to coding challenges, as interviewers will be interested in how you tackle problems rather than just the final solution.

Prepare for System Design Questions

As a Machine Learning Engineer, you may be asked to design systems that can handle large-scale data processing and model deployment. Familiarize yourself with best practices in system design, including scalability, reliability, and maintainability. Be prepared to discuss how you would approach designing a machine learning system, including considerations for data pipelines, model training, and monitoring.

Showcase Your Project Experience

Be ready to discuss your previous projects in detail, especially those related to machine learning and data processing. Highlight your contributions, the challenges you faced, and the outcomes of your work. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions clearly.

Align with Company Values

Relativity places a strong emphasis on its core values. Research these values and think about how your experiences and work ethic align with them. Be prepared to answer questions about why you want to work at Relativity and how you can contribute to the company culture. Demonstrating a good cultural fit can be just as important as technical skills.

Communicate Effectively

Throughout the interview process, focus on clear and concise communication. When answering questions, take a moment to gather your thoughts before responding. If you encounter a challenging question, don’t hesitate to ask for clarification or to think aloud as you work through your answer. This shows your problem-solving approach and helps interviewers understand your thought process.

Follow Up and Seek Feedback

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity. If you receive a rejection, don’t hesitate to reach out for feedback. While it may not always be provided, showing your willingness to learn and improve can leave a positive impression.

By preparing thoroughly and approaching the interview with confidence, you can position yourself as a strong candidate for the Machine Learning Engineer role at Relativity. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Relativity. The interview process will likely assess your technical skills in machine learning, algorithms, and programming, as well as your ability to work collaboratively within a team. Be prepared to discuss your past experiences and how they relate to the role, as well as demonstrate your problem-solving abilities through coding challenges.

Machine Learning

1. How would you design a machine learning system for a client while ensuring data privacy?

This question assesses your understanding of ethical considerations in machine learning and your ability to design systems that respect user privacy.

How to Answer

Discuss the importance of data anonymization, secure data storage, and compliance with regulations like GDPR. Highlight your approach to balancing model performance with privacy concerns.

Example

"I would start by anonymizing the data to remove personally identifiable information. I would also implement strict access controls and encryption for data storage. Additionally, I would ensure compliance with regulations like GDPR by conducting regular audits and using techniques like differential privacy to protect user data while still allowing for effective model training."

2. Describe your experience with model evaluation and selection.

This question evaluates your practical experience in assessing machine learning models.

How to Answer

Explain the metrics you use for evaluation, such as accuracy, precision, recall, and F1 score. Discuss how you select the best model based on these metrics.

Example

"I typically use a combination of accuracy, precision, and recall to evaluate models, depending on the problem at hand. For instance, in a classification task, I would prioritize precision if false positives are costly. I also use cross-validation to ensure that my model generalizes well to unseen data."

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Define both terms clearly and provide examples of each type of learning.

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, where the model tries to find patterns or groupings, such as clustering customers based on purchasing behavior."

4. What techniques do you use for feature selection?

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

How to Answer

Discuss various techniques like recursive feature elimination, LASSO regression, or tree-based methods, and explain when you would use each.

Example

"I often use recursive feature elimination to iteratively remove features and assess model performance. Additionally, I find LASSO regression useful for both feature selection and regularization, especially when dealing with high-dimensional data."

Algorithms

1. Describe a time you optimized an algorithm for better performance.

This question evaluates your problem-solving skills and experience with algorithm optimization.

How to Answer

Provide a specific example, detailing the initial performance, the changes you made, and the results.

Example

"In a previous project, I optimized a sorting algorithm from O(n^2) to O(n log n) by switching from bubble sort to quicksort. This change significantly reduced processing time, allowing us to handle larger datasets efficiently."

2. How do you approach solving a complex algorithmic problem?

This question assesses your problem-solving methodology.

How to Answer

Outline your approach, including breaking down the problem, considering edge cases, and testing your solution.

Example

"I start by breaking the problem into smaller, manageable parts and identifying edge cases. I then draft a plan for the algorithm, implement it, and test it thoroughly with various inputs to ensure it handles all scenarios."

3. Can you explain the concept of Big O notation?

This question tests your understanding of algorithm efficiency.

How to Answer

Define Big O notation and provide examples of common complexities.

Example

"Big O notation describes the upper limit of an algorithm's running time as the input size grows. For example, a linear search has a complexity of O(n), while a binary search has a complexity of O(log n), making it much more efficient for large datasets."

4. What is the difference between depth-first search and breadth-first search?

This question evaluates your knowledge of graph algorithms.

How to Answer

Explain both search strategies and their use cases.

Example

"Depth-first search (DFS) explores as far down a branch as possible before backtracking, making it useful for pathfinding in mazes. In contrast, breadth-first search (BFS) explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which is ideal for finding the shortest path in unweighted graphs."

Programming

1. Write a function to reverse a linked list.

This question tests your coding skills and understanding of data structures.

How to Answer

Discuss your thought process before writing the code, and explain the logic behind your implementation.

Example

"I would use an iterative approach to reverse the linked list by maintaining three pointers: previous, current, and next. This allows me to traverse the list while reversing the links."

2. How do you handle exceptions in your code?

This question assesses your coding best practices.

How to Answer

Explain your approach to error handling and the importance of writing robust code.

Example

"I use try-catch blocks to handle exceptions gracefully, ensuring that my application can recover from errors without crashing. I also log errors for debugging purposes and provide meaningful error messages to users."

3. Can you explain the SOLID principles of object-oriented design?

This question tests your knowledge of software design principles.

How to Answer

Define each principle and provide examples of how you apply them in your work.

Example

"SOLID principles are a set of design principles that help create maintainable and scalable software. For instance, the Single Responsibility Principle states that a class should have only one reason to change, which I apply by ensuring that my classes are focused on a single task."

4. Describe your experience with version control systems.

This question evaluates your familiarity with collaborative coding practices.

How to Answer

Discuss your experience with version control systems like Git and how you use them in team projects.

Example

"I regularly use Git for version control, employing branching strategies to manage features and bug fixes. I also conduct code reviews through pull requests to ensure code quality and facilitate knowledge sharing within the team."