Forge Machine Learning Engineer Interview Questions + Guide in 2025

Forge is a technology innovator in the private market, dedicated to empowering individuals and organizations to engage with private company investments through a sophisticated platform.

As a Machine Learning Engineer at Forge, you will be instrumental in the hands-on development and deployment of AI products, leveraging internal AI models and machine learning pipelines. Key responsibilities include designing and maintaining a robust AI/ML platform, leading agile workstreams from requirement gathering to actionable task planning, and mentoring team members on best practices in architecture and software development. You will collaborate closely with Product, Research, and Engineering teams to ensure seamless integration of AI technologies into Forge's products, setting high standards for data quality and model performance. The ideal candidate will possess extensive experience in MLOps, machine learning engineering, and data science, with a strong background in deploying diverse models and leading small teams. Experience in the Fintech or Private Markets space will be particularly valuable, as will familiarity with cloud platforms and programming languages.

This guide will equip you with insights into the role and expectations at Forge, enhancing your preparedness for a successful interview.

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

1. Initial Screening

The process begins with an initial screening, which is usually a phone interview with a recruiter. This conversation lasts about 30 minutes and serves to gauge your interest in the role, discuss your background, and evaluate your alignment with Forge's values. The recruiter will also provide insights into the company culture and the expectations for the Machine Learning Engineer position.

2. Coding Challenge

Following the initial screening, candidates are often required to complete a coding challenge. This challenge is designed to test your problem-solving abilities and proficiency in data structures and algorithms (DSA). It may involve both frontend and backend tasks, reflecting the diverse skill set required for the role. Candidates should be prepared to demonstrate their coding skills in a practical context.

3. Technical Interview

The technical interview is the next step in the process, typically conducted via video call. In this round, you will meet with a senior engineer or a technical lead who will assess your knowledge of machine learning concepts, MLOps practices, and relevant programming languages such as Python and SQL. Expect to discuss your previous projects, the models you've developed, and your approach to deploying machine learning solutions.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which consists of multiple rounds—usually five one-on-one interviews. These interviews will cover a range of topics, including advanced machine learning techniques, system design, and behavioral questions. You will have the opportunity to interact with various team members, including product managers and engineers, to evaluate how well you collaborate and communicate within a team setting. Each interview typically lasts around 45 minutes, allowing for in-depth discussions.

Throughout the interview process, Forge emphasizes a welcoming and engaging atmosphere, ensuring that candidates feel comfortable and valued.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

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

Embrace the Collaborative Spirit

Forge values teamwork and collaboration, so be prepared to discuss your experiences working in cross-functional teams. Highlight instances where you partnered with product, research, or engineering teams to deliver successful AI/ML projects. Show that you can communicate complex technical concepts to non-technical stakeholders, as this will be crucial in your role.

Showcase Your Technical Expertise

Given the emphasis on MLOps and machine learning pipelines, ensure you can articulate your experience with deploying models from proof of concept to production. Be ready to discuss specific tools and technologies you’ve used, such as AWS, Python, and CI/CD practices. Prepare to demonstrate your understanding of data quality tracking and model performance evaluation, as these are key responsibilities in the role.

Prepare for a Multi-Round Interview Process

Expect a structured interview process that includes coding challenges and multiple rounds of technical interviews. Practice coding problems that involve data structures and algorithms, as well as system design questions that reflect real-world applications of machine learning. Familiarize yourself with the types of questions that may arise in each round, and be ready to showcase your problem-solving skills under pressure.

Highlight Your Leadership and Mentorship Skills

As a Tech Lead, your ability to mentor and guide others will be critical. Prepare examples of how you have successfully led teams, provided coaching, and fostered a culture of learning. Discuss your approach to managing projects and how you ensure that your team remains agile and focused on delivering high-quality results.

Align with Forge’s Values

Forge emphasizes being bold, humble, and accountable. Reflect on how these values resonate with your personal and professional experiences. Be prepared to share stories that illustrate your alignment with these principles, as cultural fit is just as important as technical skills in the hiring process.

Stay Informed About Industry Trends

Given Forge's focus on private markets and fintech, staying updated on industry trends and challenges will give you an edge. Be ready to discuss how emerging technologies in AI/ML can address current market needs and enhance Forge's offerings. This will demonstrate your proactive approach and genuine interest in contributing to the company's mission.

Practice a Positive Mindset

Many candidates have noted the welcoming atmosphere during interviews at Forge. Approach your interview with a positive attitude, and be open to engaging in meaningful conversations. This will not only help you feel more comfortable but also allow you to connect with your interviewers on a personal level.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great cultural fit for Forge. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Forge. The interview process will likely assess your technical skills in machine learning, your experience with MLOps, and your ability to collaborate effectively with cross-functional teams. Be prepared to demonstrate your knowledge of AI technologies, data quality evaluation, and your leadership capabilities.

Machine Learning

1. Can you explain the differences between supervised, unsupervised, and reinforcement learning?

Understanding the fundamental types of machine learning is crucial for any ML engineer role.

How to Answer

Clearly define each type of learning, providing examples of algorithms and use cases for each. Highlight your experience with these methods in practical applications.

Example

“Supervised learning involves training a model on labeled data, such as using regression or classification algorithms. Unsupervised learning, on the other hand, deals with unlabeled data, often employing clustering techniques like K-means. Reinforcement learning is about training models through trial and error, where an agent learns to make decisions by receiving rewards or penalties, commonly used in game AI.”

2. Describe a machine learning project you led from conception to deployment. What challenges did you face?

This question assesses your project management and technical skills in a real-world context.

How to Answer

Outline the project scope, your role, the technologies used, and the challenges encountered. Emphasize your problem-solving skills and how you overcame obstacles.

Example

“I led a project to develop a predictive maintenance model for a manufacturing client. We faced challenges with data quality and integration from multiple sources. By implementing a robust data cleaning pipeline and collaborating closely with the engineering team, we successfully deployed the model, which reduced downtime by 20%.”

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

This question tests your understanding of model evaluation metrics and techniques.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Mention the importance of cross-validation and how you apply these metrics in your projects.

Example

“I evaluate model performance using metrics like accuracy and F1 score, depending on the problem type. For classification tasks, I also consider precision and recall to understand the trade-offs. I always use cross-validation to ensure that the model generalizes well to unseen data.”

4. What techniques do you use for feature selection and engineering?

Feature selection is critical for improving model performance and interpretability.

How to Answer

Explain your approach to selecting relevant features, including techniques like recursive feature elimination, LASSO, or tree-based methods. Discuss how you engineer new features based on domain knowledge.

Example

“I use techniques like recursive feature elimination and LASSO for feature selection, as they help in identifying the most impactful features. Additionally, I leverage domain knowledge to engineer new features, such as creating interaction terms or aggregating data over time, which can significantly enhance model performance.”

5. Can you discuss your experience with deploying machine learning models in production?

This question assesses your practical experience with MLOps and deployment strategies.

How to Answer

Detail your experience with CI/CD pipelines, model versioning, and monitoring in production. Highlight any specific tools or platforms you have used.

Example

“I have deployed machine learning models using CI/CD pipelines with tools like Jenkins and GitHub Actions. I ensure that models are versioned using MLflow and monitored post-deployment for performance drift, allowing for timely updates and retraining as necessary.”

MLOps

1. What is your experience with MLOps, and how do you implement it in your projects?

This question evaluates your understanding and practical experience with MLOps practices.

How to Answer

Discuss your familiarity with MLOps principles, tools, and how you integrate them into your workflow to streamline model deployment and monitoring.

Example

“I have extensive experience with MLOps, implementing practices such as continuous integration and deployment for machine learning models. I utilize tools like Kubeflow for orchestration and monitoring, ensuring that our models are not only deployed efficiently but also maintained and updated based on performance metrics.”

2. How do you ensure data quality in your machine learning pipelines?

Data quality is essential for building reliable models.

How to Answer

Explain your strategies for data validation, cleaning, and monitoring throughout the data pipeline.

Example

“I ensure data quality by implementing validation checks at each stage of the pipeline, using tools like Great Expectations for data profiling. Regular audits and monitoring help catch anomalies early, ensuring that the data fed into the models is accurate and reliable.”

3. Describe your experience with cloud platforms for machine learning.

This question assesses your familiarity with cloud services relevant to ML.

How to Answer

Mention specific cloud platforms you have used, the services they offer for ML, and how you have leveraged them in your projects.

Example

“I have worked extensively with AWS, utilizing services like SageMaker for model training and deployment. I also use Snowflake for data warehousing, which allows for efficient data retrieval and processing, essential for our machine learning workflows.”

4. What role does version control play in your machine learning projects?

Version control is crucial for collaboration and reproducibility in ML projects.

How to Answer

Discuss how you use version control systems to manage code, data, and model versions, and the importance of this practice in team settings.

Example

“I use Git for version control to manage code and collaborate with my team effectively. For model versioning, I employ tools like DVC, which allows us to track changes in datasets and models, ensuring reproducibility and facilitating collaboration across different team members.”

5. How do you handle model drift and retraining in production?

This question evaluates your understanding of maintaining model performance over time.

How to Answer

Explain your approach to monitoring model performance and the strategies you use for retraining when necessary.

Example

“I monitor model performance using metrics like accuracy and F1 score in production. When I detect performance degradation, I initiate a retraining process using the latest data, ensuring that the model remains relevant and accurate over time.”