Harness Machine Learning Engineer Interview Questions + Guide in 2025

Harness is a high-growth startup focused on revolutionizing the software delivery market, enabling developers to deliver code reliably and efficiently while enhancing innovation and user experience.

As a Machine Learning Engineer at Harness, you will play a critical role in designing and implementing machine learning projects that impact the software delivery lifecycle. Your key responsibilities will include collaborating with cross-functional teams to develop custom ML models and algorithms tailored to various datasets, addressing complex product development problems, and working closely with engineers to deploy and maintain ML models in a production environment. A strong foundation in algorithms, particularly in Python, is essential, alongside expertise in building machine learning infrastructure and applying machine learning algorithms to drive product development. You will also need to demonstrate excellent communication skills to effectively convey technical concepts and collaborate with diverse teams.

To excel in this role, familiarity with statistical methods, machine learning principles, and algorithm design will be crucial. Additionally, a proactive attitude towards continuous learning and adaptation to new technologies aligns with Harness's fast-paced environment. Your experience and background in tackling real-world challenges through innovative ML applications will set you apart.

This guide aims to prepare you for the interview process by providing insights into the expectations and technical depth required for the role of Machine Learning Engineer at Harness. Understanding the nuances of the position and aligning your experiences with the company's mission will enhance your candidacy.

The interview process for a Machine Learning Engineer at Harness is designed to assess both technical skills and cultural fit within the organization. It typically consists of several structured rounds, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Screening

The process begins with an initial screening, usually conducted by a recruiter. This round typically lasts around 30-45 minutes and focuses on understanding your background, experience, and motivation for applying to Harness. The recruiter will also provide insights into the company culture and the specifics of the role.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home assignment or a coding challenge that you will have 48 hours to complete. The assessment is designed to evaluate your proficiency in algorithms, data structures, and machine learning concepts, particularly focusing on your ability to implement solutions in Python.

3. Technical Interviews

Candidates who successfully pass the technical assessment will move on to a series of technical interviews. Typically, there are two to three rounds of technical interviews, each lasting about an hour. These interviews will cover a range of topics, including data structures and algorithms, machine learning principles, and system design. Expect to solve coding problems on platforms like LeetCode, with a focus on medium to hard difficulty levels. Interviewers may also ask questions related to your previous projects and experiences in building machine learning models.

4. Design and System Architecture Round

In this round, candidates will be asked to design a system or a machine learning model. You may be tasked with discussing the architecture of a project you have worked on or designing a new solution from scratch. This round assesses your ability to think critically about system design and your understanding of how to deploy and maintain machine learning models in a production environment.

5. Managerial and Behavioral Interview

The final round typically includes a managerial interview, where you will meet with a hiring manager or senior team members. This round focuses on your past experiences, how you handle hypothetical scenarios, and your approach to teamwork and collaboration. Behavioral questions will be asked to gauge your cultural fit within the company and your alignment with Harness's values.

Throughout the interview process, it is essential to communicate your thought process clearly and demonstrate your problem-solving skills.

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

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

Prepare for a Comprehensive Interview Process

Harness is known for its thorough interview process, which typically includes multiple rounds focusing on both technical and managerial aspects. Be ready for a mix of coding challenges, system design questions, and discussions about your previous experiences. Familiarize yourself with common data structures and algorithms, as well as system design principles, since these are frequently assessed. Practice coding problems on platforms like LeetCode, especially those related to graph algorithms, dynamic programming, and binary search, as these have been highlighted in past interviews.

Showcase Your Technical Expertise

As a Machine Learning Engineer, you will need to demonstrate a strong foundation in algorithms, Python, and machine learning principles. Brush up on your knowledge of machine learning frameworks like TensorFlow and PyTorch, and be prepared to discuss your experience in building ML infrastructure. You may be asked to design and implement ML models, so be ready to explain your thought process and the rationale behind your design choices. Additionally, be prepared to discuss how you would deploy and maintain these models in a production environment.

Communicate Clearly and Effectively

Harness values excellent communication skills, so practice articulating your thoughts clearly and concisely. During technical discussions, explain your reasoning and approach as you solve problems. This not only demonstrates your technical knowledge but also shows your ability to collaborate with team members. Be candid about your background and expertise, as transparency is appreciated in the interview process.

Engage with the Interviewers

The interviewers at Harness are known to be friendly and approachable. Use this to your advantage by engaging them in conversation. Ask insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you gauge if Harness is the right fit for you. Remember, interviews are a two-way street.

Be Ready for Behavioral Questions

In addition to technical assessments, expect behavioral questions that assess your fit within the company culture. Reflect on your past experiences and be prepared to discuss challenges you've faced, how you've worked in teams, and your approach to problem-solving. Harness values a collaborative environment, so highlight instances where you've successfully worked with others to achieve a common goal.

Understand the Company Culture

Harness is a high-growth startup that emphasizes innovation and collaboration. Familiarize yourself with their mission and values, and think about how your personal values align with theirs. Be prepared to discuss why you want to work at Harness and how you can contribute to their goals. Showing that you understand and resonate with the company culture can set you apart from other candidates.

Follow Up and Stay Engaged

After your interviews, consider sending a thank-you note to express your appreciation for the opportunity to interview. This not only reinforces your interest in the position but also keeps you on the interviewers' radar. If you have any questions or need clarification about the process, don’t hesitate to reach out to your recruiter.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Harness. The interview process will likely focus on your technical expertise in machine learning, algorithms, and your ability to work collaboratively in a fast-paced environment. Be prepared to discuss your previous experiences, problem-solving approaches, and how you can contribute to the company's mission of enhancing software delivery.

Algorithms

1. Can you explain the concept of time complexity and space complexity?

Understanding time and space complexity is crucial for evaluating the efficiency of algorithms.

How to Answer

Discuss the definitions of both concepts and provide examples of how they can impact algorithm performance.

Example

“Time complexity measures the amount of time an algorithm takes to complete as a function of the input size, while space complexity measures the amount of memory space required. For instance, a binary search algorithm has a time complexity of O(log n) and a space complexity of O(1), making it efficient for large datasets.”

2. Describe the difference between a stack and a queue.

This question tests your understanding of data structures and their applications.

How to Answer

Explain the fundamental differences in how data is stored and accessed in both structures.

Example

“A stack follows a Last In First Out (LIFO) principle, meaning the last element added is the first to be removed, while a queue follows a First In First Out (FIFO) principle, where the first element added is the first to be removed. Stacks are often used in function call management, while queues are used in scheduling tasks.”

3. How would you implement a binary search algorithm?

This question assesses your coding skills and understanding of search algorithms.

How to Answer

Outline the steps of the binary search algorithm and provide a brief code example.

Example

“To implement a binary search, I would first sort the array. Then, I would repeatedly divide the search interval in half, checking if the target value is equal to the middle element, less than, or greater than it. If it’s less, I search the left half; if it’s greater, I search the right half until I find the target or exhaust the search space.”

4. Explain the Morris Order Traversal technique.

This question tests your knowledge of tree traversal algorithms.

How to Answer

Discuss the Morris traversal method and its advantages.

Example

“Morris Order Traversal is a way to traverse a binary tree without using additional space for a stack or recursion. It modifies the tree structure temporarily by creating links to the predecessor nodes, allowing for an in-order traversal in O(n) time and O(1) space.”

5. Can you solve a problem involving the number of connected components in a graph?

This question evaluates your understanding of graph theory.

How to Answer

Explain the approach you would take to solve the problem, including any algorithms you would use.

Example

“To find the number of connected components in a graph, I would use Depth First Search (DFS) or Breadth First Search (BFS). Starting from an unvisited node, I would traverse all reachable nodes, marking them as visited, and increment a counter for each traversal initiated from an unvisited node.”

Machine Learning

1. What is your experience with building machine learning models in Python?

This question assesses your practical experience with machine learning frameworks.

How to Answer

Discuss specific projects or models you have built, including the libraries used.

Example

“I have built several machine learning models using Python, primarily with libraries like scikit-learn and TensorFlow. For instance, I developed a predictive model for customer churn using logistic regression, where I preprocessed the data, selected features, and evaluated the model’s performance using cross-validation.”

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

This question evaluates your understanding of model optimization.

How to Answer

Explain the methods you use for feature selection and their importance.

Example

“I approach feature selection by first analyzing the correlation between features and the target variable. I use techniques like Recursive Feature Elimination (RFE) and feature importance from tree-based models to identify the most significant features, which helps improve model performance and reduce overfitting.”

3. Can you describe a machine learning project you worked on from start to finish?

This question allows you to showcase your project management skills.

How to Answer

Outline the project’s objectives, your role, the methodologies used, and the outcomes.

Example

“I worked on a project to predict sales for a retail company. I started by gathering and cleaning the data, then performed exploratory data analysis to identify trends. I built a time series forecasting model using ARIMA, which improved the accuracy of sales predictions by 20%. Finally, I presented the findings to stakeholders, highlighting actionable insights.”

4. What are some common challenges you face when deploying machine learning models?

This question assesses your understanding of the deployment process.

How to Answer

Discuss the challenges and how you address them.

Example

“Common challenges include ensuring model performance in production, handling data drift, and integrating the model with existing systems. I address these by implementing monitoring tools to track model performance and retraining schedules to adapt to new data patterns.”

5. How do you stay updated with the latest advancements in machine learning?

This question evaluates your commitment to continuous learning.

How to Answer

Mention specific resources, communities, or conferences you engage with.

Example

“I stay updated by following leading machine learning journals, participating in online courses, and attending conferences like NeurIPS and ICML. I also contribute to open-source projects on GitHub, which helps me learn from the community and apply new techniques in my work.”