Handshake Machine Learning Engineer Interview Questions + Guide in 2025

Handshake is a platform dedicated to connecting students with employers, fostering opportunities, and democratizing career paths for millions.

As a Machine Learning Engineer at Handshake, you'll play a crucial role in utilizing AI and machine learning technologies to enhance user experiences and drive the company's mission forward. Your key responsibilities will include developing and implementing innovative machine learning algorithms and pipelines that connect students seeking internships and jobs with potential employers. You'll be expected to collaborate closely with product managers, engineering teams, and other stakeholders to create data-driven solutions that improve the platform's functionality and user engagement.

To excel in this role, candidates should possess a strong foundation in algorithms and machine learning principles, with a substantial focus on Python as the primary programming language. Proficiency in distributed systems and experience with analytics tools like Spark and SQL will be essential. A background in statistics and the ability to communicate complex concepts effectively to diverse audiences will also set you apart. Handshake values a culture of mentorship and collaboration, so being a supportive and engaging team member is crucial.

This guide is designed to help you prepare for your interview by providing insights into the expectations and skills necessary for success at Handshake. By understanding the role and the company culture, you’ll be better equipped to demonstrate your fit and value during the interview process.

The interview process for a Machine Learning Engineer at Handshake is designed to be thorough and engaging, ensuring that candidates not only possess the necessary technical skills but also align with the company's culture and mission. The process typically unfolds over several stages, allowing for a comprehensive evaluation of both technical and interpersonal capabilities.

1. Initial Phone Screen

The first step in the interview process is a 30-minute phone call with a recruiter. This conversation serves as an opportunity for the recruiter to learn about your background, experiences, and motivations while also providing insights into the role and Handshake's culture. Expect to discuss your resume and how your skills align with the company's mission of democratizing opportunities for students.

2. Technical Screening

Following the initial screen, candidates typically undergo a technical screening, which may last about an hour. This stage often involves a live coding challenge where you will be asked to solve algorithmic problems or work on a coding exercise relevant to machine learning. You may also be required to demonstrate your proficiency in Python or other programming languages, as well as your understanding of algorithms and data structures.

3. Take-Home Assignment

Candidates may be given a take-home assignment that involves building a small project or completing a coding challenge. This assignment is designed to assess your practical skills in developing machine learning models or applications. You will be expected to submit your work before moving on to the next stage.

4. Virtual Onsite Interview

The virtual onsite interview is a more extensive evaluation, typically lasting around four to five hours. This stage is divided into several parts: - Coding Exercise: You will work on a coding project, often involving pair programming with engineers from Handshake. This collaborative environment allows interviewers to assess your coding style, problem-solving approach, and ability to communicate effectively with team members. - System Design Interview: In this segment, you will be asked to design a system or architecture for a machine learning application. This is an opportunity to showcase your understanding of distributed systems, data pipelines, and machine learning frameworks. - Behavioral Interview: Expect to engage in discussions about your past experiences, teamwork, and how you handle challenges. This part of the interview is crucial for assessing cultural fit and your alignment with Handshake's values.

5. Final Interview with Hiring Manager

The final step often involves a conversation with the hiring manager or senior leadership. This interview focuses on your long-term vision, leadership qualities, and how you can contribute to the team and the company's mission. It’s also a chance for you to ask questions about the team dynamics and future projects.

Throughout the process, candidates are encouraged to demonstrate their passion for machine learning and their commitment to Handshake's mission. The interviewers are known for being supportive and providing constructive feedback, making the experience both challenging and rewarding.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that assess your technical skills and cultural fit.

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

Emphasize Your Technical Expertise

As a Machine Learning Engineer, your technical skills are paramount. Focus on your experience with algorithms, particularly in the context of machine learning and natural language processing. Be prepared to discuss specific projects where you developed or implemented algorithms, and how they contributed to the overall success of the project. Highlight your proficiency in Python, as it is a key language for this role, and be ready to demonstrate your understanding of machine learning frameworks and libraries.

Prepare for Collaborative Problem-Solving

The interview process at Handshake often includes pair programming and collaborative coding exercises. Approach these sessions as opportunities to showcase not just your technical skills, but also your ability to communicate and work effectively with others. Practice articulating your thought process clearly while coding, and be open to feedback and suggestions from your interviewers. This will demonstrate your collaborative spirit and adaptability, which are highly valued in Handshake's culture.

Understand the Company’s Mission and Culture

Handshake is deeply committed to democratizing opportunities for students and early-career professionals. Familiarize yourself with their mission and values, and think about how your personal experiences and professional goals align with them. During the interview, express your enthusiasm for their mission and how you can contribute to it through your work in machine learning. This alignment will resonate well with the interviewers and show that you are not just looking for a job, but are genuinely interested in being part of their mission.

Be Ready for Behavioral Questions

Expect to encounter behavioral questions that assess your fit within the company culture. Prepare examples from your past experiences that demonstrate your problem-solving abilities, teamwork, and leadership skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your actions. This will help you present yourself as a well-rounded candidate who can thrive in Handshake's collaborative environment.

Leverage Feedback Opportunities

Candidates have noted that Handshake's interview process includes constructive feedback, even for those who do not receive an offer. Take advantage of this by asking for feedback on your performance during the interview. This not only shows your willingness to learn and grow but also leaves a positive impression on the interviewers, reinforcing your interest in the role and the company.

Prepare for Technical Challenges

The technical portion of the interview may include coding challenges and system design questions. Review common algorithms and data structures, and practice coding problems that require you to think critically and solve complex issues. Familiarize yourself with system design principles, especially as they relate to machine learning applications. Being well-prepared will help you approach these challenges with confidence.

Stay Positive and Engaged

Throughout the interview process, maintain a positive and engaged demeanor. Candidates have reported that the interviewers at Handshake are friendly and supportive, so reciprocate that energy. Show enthusiasm for the role and the company, and be sure to ask thoughtful questions that reflect your interest in the position and the team dynamics. This will help you build rapport with your interviewers and leave a lasting impression.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Handshake. The interview process will assess your technical skills in machine learning, algorithms, and coding, as well as your ability to collaborate and communicate effectively within a team. Be prepared to discuss your past experiences and how they align with Handshake's mission of democratizing opportunities.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key differences, such as the presence of labeled data in supervised learning versus the absence in unsupervised learning. Provide examples like classification for supervised and clustering for unsupervised.

Example

“Supervised learning involves training a model on a labeled dataset, where the algorithm learns to predict outcomes based on input features. For instance, in a spam detection system, emails are labeled as 'spam' or 'not spam.' In contrast, unsupervised learning deals with unlabeled data, where the model identifies patterns or groupings, such as customer segmentation in marketing.”

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

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

How to Answer

Highlight a specific project, the challenges encountered, and how you overcame them. Focus on your role and the impact of the project.

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data, which I addressed by implementing collaborative filtering techniques. This improved the accuracy of our recommendations significantly, leading to a 15% increase in user engagement.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Explain how these methods help improve model generalization.

Example

“To combat overfitting, I often use techniques like cross-validation to ensure the model performs well on unseen data. Additionally, I apply regularization methods like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation.

How to Answer

Mention various metrics relevant to the type of problem (e.g., accuracy, precision, recall, F1 score for classification; RMSE, MAE for regression).

Example

“I typically use accuracy and F1 score for classification tasks, as they provide a good balance between precision and recall. For regression tasks, I prefer RMSE, as it gives a clear indication of the model's prediction error.”

Algorithms

1. Can you explain how a decision tree works?

This question assesses your understanding of fundamental algorithms.

How to Answer

Describe the structure of a decision tree and how it makes decisions based on feature values.

Example

“A decision tree splits the data into subsets based on feature values, creating branches that lead to decision nodes or leaf nodes. Each split is determined by a criterion like Gini impurity or information gain, allowing the model to make predictions based on the majority class in the leaf node.”

2. What is the purpose of a confusion matrix?

This question tests your knowledge of model evaluation.

How to Answer

Explain what a confusion matrix is and how it helps in understanding the performance of a classification model.

Example

“A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives. It helps in calculating various metrics like accuracy, precision, and recall, providing insights into the model's strengths and weaknesses.”

3. Describe the concept of gradient descent.

This question evaluates your understanding of optimization techniques.

How to Answer

Discuss how gradient descent is used to minimize the loss function in machine learning models.

Example

“Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting the model parameters in the direction of the steepest descent, determined by the negative gradient. This process continues until convergence is achieved, allowing the model to learn effectively from the training data.”

4. How would you implement a k-means clustering algorithm?

This question assesses your practical knowledge of clustering algorithms.

How to Answer

Outline the steps involved in the k-means algorithm, including initialization, assignment, and update phases.

Example

“To implement k-means clustering, I would first initialize k centroids randomly. Then, I would assign each data point to the nearest centroid, forming clusters. After that, I would update the centroids by calculating the mean of the points in each cluster. This process repeats until the centroids no longer change significantly.”

Coding and Technical Skills

1. Write a function to reverse a string in Python.

This question tests your coding skills and familiarity with Python.

How to Answer

Provide a clear and efficient solution, explaining your thought process as you code.

Example

“Here’s a simple function to reverse a string in Python: python def reverse_string(s): return s[::-1] This uses Python's slicing feature to reverse the string efficiently.”

2. How would you design a RESTful API for a machine learning model?

This question evaluates your understanding of API design and integration.

How to Answer

Discuss the key components of a RESTful API, including endpoints, request methods, and data formats.

Example

“I would design a RESTful API with endpoints for model predictions, training, and evaluation. For instance, a POST request to /predict would accept input data in JSON format and return the model's predictions. I would ensure proper error handling and documentation for ease of use.”

3. Can you explain the concept of object-oriented programming (OOP) and its principles?

This question assesses your understanding of programming paradigms.

How to Answer

Discuss the four main principles of OOP: encapsulation, inheritance, polymorphism, and abstraction.

Example

“OOP is a programming paradigm based on the concept of objects, which can contain data and methods. The four main principles are encapsulation, which restricts access to certain components; inheritance, which allows classes to inherit properties from other classes; polymorphism, which enables methods to do different things based on the object; and abstraction, which simplifies complex systems by modeling classes based on essential properties.”

4. Describe how you would optimize a SQL query.

This question tests your knowledge of database management and optimization techniques.

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize a SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I would consider adding indexes to frequently queried columns, restructuring the query to reduce complexity, and ensuring that I only select the necessary columns to minimize data retrieval time.”