Procore Technologies Machine Learning Engineer Interview Questions + Guide in 2025

Procore Technologies is at the forefront of innovation in the construction industry, providing a cloud-based platform that enhances operational efficiency and transforms how construction projects are managed.

As a Machine Learning Engineer at Procore, you will be integral to the Product & Technology Team, focusing on advancing the future of Construction Intelligence. Your role will involve working with large-scale structured and unstructured data to build and refine cutting-edge machine learning models. You will evaluate and train various machine learning and language models, including Large Language Models (LLMs) like BERT, while ensuring models are production-ready through hands-on development and maintenance of machine learning pipelines. Your work will not only involve collaborating closely with cross-functional teams—including software engineers, product managers, and data scientists—but it will also emphasize understanding user needs and business impact. Procore values a customer-centric approach, so a keen interest in addressing real-world construction challenges through AI will be crucial.

Key responsibilities will include designing and deploying APIs for predictive insights, implementing continuous integration and delivery methodologies, and leveraging both in-house and third-party machine learning tools. Success in this role requires strong programming skills in languages such as Python, Java, or C++, along with a deep understanding of the machine learning ecosystem, including frameworks like TensorFlow, PyTorch, and data management tools such as Snowflake and Databricks.

Ideal candidates will have at least 5 years of industry experience in applied machine learning, with exceptional communication skills and a passion for solving complex user problems. Familiarity with the construction industry is a plus but not a requirement.

This guide will provide you with tailored insights and strategies to prepare for your interview, helping you to stand out as a candidate who not only possesses the necessary technical skills but also aligns with Procore's values and mission.

The interview process for a Machine Learning Engineer at Procore Technologies is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and compatibility with Procore's values.

1. Initial Phone Screen

The process begins with a phone screening conducted by a recruiter. This initial conversation usually lasts around 30 minutes and focuses on understanding the candidate's background, career goals, and interest in the role. The recruiter may ask about the candidate's familiarity with Procore and its products, as well as general questions about their experience in machine learning and data engineering.

2. Technical Assessment

Following the initial screen, candidates typically undergo a technical assessment. This may involve a coding challenge or a technical interview with a member of the engineering team. The focus here is on evaluating the candidate's programming skills, understanding of machine learning concepts, and ability to solve problems relevant to the role. Candidates should be prepared to discuss their experience with machine learning frameworks and tools, as well as their approach to building and deploying models.

3. Onsite Interview

Candidates who successfully pass the technical assessment are invited for an onsite interview, which can also be conducted virtually. This stage usually consists of multiple interviews with various team members, including engineers, product managers, and possibly senior leadership. The onsite interview typically includes a mix of technical challenges, behavioral questions, and discussions about the candidate's past experiences and how they align with Procore's culture. Expect to engage in collaborative problem-solving exercises, such as pair programming or system design discussions.

4. Final Interview

The final stage may involve a more in-depth discussion with the hiring manager or a panel of interviewers. This interview often focuses on leadership qualities, long-term career aspirations, and how the candidate can contribute to the team and the company as a whole. Candidates may be asked to articulate their vision for machine learning applications within the construction industry and how they can leverage their skills to drive innovation at Procore.

Throughout the interview process, Procore places a strong emphasis on cultural fit, collaboration, and a passion for customer-centric solutions. Candidates should be prepared to demonstrate their problem-solving abilities, technical expertise, and alignment with Procore's values of openness, ownership, and optimism.

As you prepare for your interview, consider the types of questions that may arise in each stage of the process.

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

Understand Procore's Culture and Values

Procore places a strong emphasis on its core values: Openness, Ownership, and Optimism. Familiarize yourself with these values and think about how they resonate with your own work ethic and experiences. During the interview, be prepared to discuss how you embody these values in your professional life. This will not only demonstrate your alignment with the company culture but also show that you are genuinely interested in being part of their team.

Prepare for Technical and Behavioral Questions

The interview process at Procore is known to be comprehensive, often including both technical assessments and behavioral interviews. Brush up on your machine learning knowledge, particularly in areas like Large Language Models (LLMs), data engineering, and relevant programming languages (Python, Java, etc.). Additionally, be ready to discuss your past experiences, particularly those that showcase your problem-solving skills and ability to work collaboratively in a team setting.

Showcase Your Passion for Construction Technology

Given Procore's focus on the construction industry, it’s beneficial to demonstrate your understanding of the challenges and opportunities within this sector. If you have any experience or knowledge related to construction technologies, be sure to highlight that. Discuss how your skills in machine learning can contribute to solving real-world problems in construction, which will show your enthusiasm for the role and the industry.

Engage in the Interview Process

Candidates have noted that the interviewers at Procore are friendly and approachable. Use this to your advantage by engaging in a conversational manner. Ask insightful questions about the team, projects, and company direction. This not only shows your interest but also helps you gauge if Procore is the right fit for you.

Be Ready for a Collaborative Coding Challenge

Expect to participate in coding challenges that may involve pair programming or collaborative problem-solving. Practice coding problems that require you to explain your thought process clearly while coding. This will help you demonstrate not only your technical skills but also your ability to communicate effectively with team members.

Follow Up and Seek Feedback

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. If you do not hear back within the expected timeframe, don’t hesitate to follow up politely. If you receive a rejection, ask for feedback on your interview performance. This shows your commitment to personal growth and can provide valuable insights for future opportunities.

By preparing thoroughly and aligning your approach with Procore's values and expectations, you can enhance your chances of success in the interview process. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Procore Technologies. The interview process will likely assess your technical competencies, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your experience with machine learning models, data engineering, and your understanding of the construction industry.

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 input data is paired with the correct output. For instance, predicting house prices based on features like size and location is a supervised task. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, such as customer segmentation in marketing.”

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

This question tests your knowledge of model evaluation metrics.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for overall correctness, precision and recall for imbalanced datasets, and the F1 score to balance both. For binary classification, I also consider the ROC-AUC score to assess the trade-off between true positive and false positive rates.”

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

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

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them.

Example

“In a project to predict equipment failures, I faced challenges with missing data. I implemented imputation techniques and feature engineering to enhance model performance. Ultimately, we achieved a 20% increase in prediction accuracy by refining our feature set.”

4. What is overfitting, and how can you prevent it?

Understanding overfitting is essential for model training.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization on unseen data. To prevent it, I use techniques like cross-validation to ensure the model performs well on different subsets of data, and I apply regularization methods like L1 and L2 to penalize overly complex models.”

Data Engineering

1. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use mean or median imputation for numerical data, or I could opt for deletion if the missing data is minimal. For more complex datasets, I might use predictive modeling to estimate missing values.”

2. Explain the concept of feature engineering and its importance.

Feature engineering is critical in improving model performance.

How to Answer

Define feature engineering and discuss its role in enhancing model accuracy.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. It’s crucial because the right features can significantly enhance a model’s ability to learn patterns. For instance, creating interaction terms or aggregating features can provide additional insights that raw data may not reveal.”

3. What tools and technologies do you use for data processing?

This question assesses your familiarity with data engineering tools.

How to Answer

Mention specific tools and technologies you have experience with, such as Apache Spark, Hadoop, or ETL tools.

Example

“I primarily use Apache Spark for large-scale data processing due to its speed and efficiency. I also leverage ETL tools like Apache NiFi for data ingestion and transformation, ensuring that data is clean and ready for analysis.”

APIs and Deployment

1. How do you design and deploy APIs for machine learning models?

This question tests your understanding of model deployment.

How to Answer

Discuss the process of designing RESTful APIs and the tools you use for deployment.

Example

“I design RESTful APIs using Flask or FastAPI, allowing clients to interact with the model easily. For deployment, I use Docker to containerize the application, ensuring consistency across environments. I also leverage cloud services like AWS or GCP for scalable deployment.”

2. What is CI/CD, and how do you implement it in machine learning projects?

Understanding CI/CD is essential for modern software development practices.

How to Answer

Define CI/CD and explain how it applies to machine learning workflows.

Example

“CI/CD stands for Continuous Integration and Continuous Deployment. In machine learning, I implement CI/CD by automating the testing and deployment of models. This includes setting up pipelines that automatically retrain models with new data and deploy them to production, ensuring that we always have the latest version available.”

Behavioral Questions

1. Describe a time you had to work with a difficult team member. How did you handle it?

This question assesses your interpersonal skills and teamwork.

How to Answer

Provide a specific example, focusing on your approach to resolving the conflict.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to understand their perspective and shared my concerns constructively. By fostering open communication, we were able to collaborate more effectively and ultimately improved our project outcomes.”

2. Why do you want to work at Procore?

This question gauges your interest in the company and role.

How to Answer

Discuss your alignment with Procore’s mission and values, and how your skills can contribute.

Example

“I admire Procore’s commitment to innovation in the construction industry. I believe my experience in machine learning and passion for using technology to solve real-world problems align perfectly with your mission to improve the lives of those in construction. I’m excited about the opportunity to contribute to such impactful work.”