Effective Work Sample Exercises for Hiring Top Machine Learning Engineers

Machine Learning Engineers are at the forefront of transforming businesses through artificial intelligence and data-driven solutions. Finding the right talent for this role is critical, as these professionals bridge the gap between theoretical data science and practical software engineering. The best ML Engineers not only understand complex algorithms but can also implement them in production environments, communicate effectively with stakeholders, and continuously optimize solutions.

Traditional interviews often fail to reveal a candidate's true capabilities in this multifaceted role. While technical questions can assess theoretical knowledge, they rarely demonstrate how candidates approach real-world problems or implement solutions under constraints. This is where carefully designed work samples become invaluable.

Work samples for Machine Learning Engineers should evaluate four critical areas: technical implementation skills, system design thinking, problem-solving abilities, and communication effectiveness. By observing candidates tackle realistic challenges, hiring teams can gain deeper insights into their practical capabilities and working style.

The following exercises are designed to simulate actual tasks ML Engineers face daily. They provide a window into how candidates approach problems, implement solutions, optimize models, and communicate complex concepts. When used as part of a structured interview process, these work samples significantly improve hiring decisions by revealing capabilities that traditional interviews might miss.

Activity #1: Model Implementation and Evaluation

This exercise evaluates a candidate's hands-on machine learning skills, including their ability to implement appropriate algorithms, preprocess data effectively, and evaluate model performance. It reveals their coding practices, familiarity with ML frameworks, and approach to model selection and validation.

Directions for the Company:

• Prepare a dataset relevant to your business domain (or use a public dataset if necessary) that requires cleaning and feature engineering.
• Create a Jupyter notebook or Google Colab document with clear instructions and starter code.
• Provide access to the dataset and any necessary documentation about the data.
• Allow candidates 2-3 hours to complete the exercise, either as a take-home assignment or during an on-site interview.
• Ensure the task is scoped appropriately—it should be challenging but completable within the time frame.
• Have a senior ML engineer review the solution using a standardized rubric that evaluates code quality, model selection, feature engineering approach, and evaluation methodology.

Directions for the Candidate:

• You will be provided with a dataset and a business problem to solve using machine learning.
• Clean and preprocess the data, explaining your decisions for handling missing values, outliers, and feature transformations.
• Implement at least two different machine learning models to address the problem.
• Evaluate the models using appropriate metrics and explain which one you would recommend for production use and why.
• Document your code clearly and be prepared to explain your approach.
• Your solution will be evaluated on code quality, model performance, feature engineering decisions, and the clarity of your documentation.

Feedback Mechanism:

• After reviewing the solution, provide specific feedback on one aspect the candidate executed well (e.g., "Your feature engineering approach was particularly effective because…").
• Offer one constructive suggestion for improvement (e.g., "Your model evaluation could be enhanced by considering these additional metrics…").
• Ask the candidate to spend 10-15 minutes implementing the suggested improvement or explaining how they would approach it if given more time.
• Observe how receptive they are to feedback and their ability to iterate on their solution.

Activity #2: ML System Design and Architecture

This exercise assesses a candidate's ability to design scalable, production-ready machine learning systems. It evaluates their understanding of ML infrastructure, deployment considerations, and how ML components integrate with broader software systems.

Directions for the Company:

• Create a realistic business scenario that requires designing an end-to-end ML system (e.g., a recommendation engine, fraud detection system, or predictive maintenance solution).
• Prepare a document outlining the business requirements, expected scale, and any constraints.
• Provide whiteboarding tools (physical or digital) for the candidate to sketch their design.
• Allocate 45-60 minutes for this exercise during the interview.
• Have engineering and product stakeholders participate to evaluate both technical soundness and business alignment.
• Prepare probing questions about scalability, monitoring, and handling edge cases.

Directions for the Candidate:

• Review the business scenario and requirements provided.
• Design an end-to-end machine learning system architecture that addresses these requirements.
• Your design should include:
• Data ingestion and preprocessing pipeline
• Model training infrastructure
• Deployment and serving strategy
• Monitoring and retraining approach
• Sketch your architecture using the provided tools and be prepared to explain each component.
• Consider scalability, reliability, and maintainability in your design.
• Be ready to discuss tradeoffs in your design decisions and how you would handle potential failure modes.

Feedback Mechanism:

• Provide feedback on a strength in their design (e.g., "Your approach to model versioning shows strong production experience").
• Suggest one area where the design could be improved (e.g., "Consider how you might handle concept drift in this system").
• Give the candidate 10 minutes to revise their design based on this feedback.
• Evaluate their ability to incorporate feedback and adapt their thinking.

Activity #3: Model Debugging and Optimization

This exercise evaluates a candidate's troubleshooting abilities and their approach to optimizing machine learning models. It reveals their diagnostic skills, familiarity with common ML pitfalls, and knowledge of optimization techniques.

Directions for the Company:

• Prepare a pre-built machine learning model with intentional issues (e.g., overfitting, data leakage, poor feature selection, or inefficient implementation).
• Include the model code, training/evaluation data, and current performance metrics.
• Document the expected performance and the issues that need to be addressed.
• Allow 60-90 minutes for this exercise.
• Prepare hints that can be provided if candidates get stuck, to keep the exercise productive.
• Have a rubric ready to evaluate the systematic nature of their debugging approach and the effectiveness of their optimizations.

Directions for the Candidate:

• You will be provided with an existing machine learning model that is underperforming.
• Your task is to:

1. Analyze the model and identify potential issues affecting its performance
2. Implement improvements to address these issues
3. Document your debugging process and the rationale behind your changes

• Focus on both model accuracy and computational efficiency.
• You may use any diagnostic tools or techniques you're familiar with.
• Be prepared to explain your thought process, including dead ends you explored.
• Your solution will be evaluated on your systematic approach to problem-solving, the improvements achieved, and your explanation of the underlying issues.

Feedback Mechanism:

• Highlight one effective debugging technique the candidate employed (e.g., "Your use of feature importance analysis quickly identified the key issue").
• Suggest one additional area they could investigate or an alternative approach (e.g., "Consider how regularization might address the variance issues you identified").
• Give the candidate 15 minutes to implement this suggestion or explain how they would approach it.
• Assess their receptiveness to feedback and ability to incorporate new approaches.

Activity #4: Technical Communication and Stakeholder Presentation

This exercise evaluates a candidate's ability to communicate complex machine learning concepts to both technical and non-technical stakeholders. It assesses their skill in translating technical details into business value and their effectiveness in collaborative environments.

Directions for the Company:

• Create a scenario where an ML model has been developed and needs to be presented to a mixed audience of technical and business stakeholders.
• Provide documentation about the model, including its purpose, methodology, performance metrics, and business impact.
• Include some technical details that would need to be simplified for non-technical audience members.
• Allocate 30 minutes for preparation and 15-20 minutes for the presentation, followed by Q&A.
• Assemble a panel with both technical and non-technical interviewers to evaluate different aspects of the communication.
• Prepare questions that both technical and business stakeholders might ask.

Directions for the Candidate:

• Review the provided materials about a machine learning model that has been developed.
• Prepare a 15-minute presentation that explains:
• The business problem the model addresses
• How the model works (at an appropriate level for a mixed audience)
• The model's performance and limitations
• Recommendations for deployment or next steps
• Your audience will include both technical team members and business stakeholders.
• Create any visual aids you feel would help communicate these concepts effectively.
• Be prepared to answer questions from both technical and business perspectives.
• You will be evaluated on clarity, ability to adjust technical depth appropriately, and effectiveness in communicating value and limitations.

Feedback Mechanism:

• Provide feedback on an aspect of their communication that was particularly effective (e.g., "Your analogy made the complex algorithm accessible to non-technical stakeholders").
• Suggest one area for improvement (e.g., "The technical details about feature importance could be more clearly connected to business outcomes").
• Ask the candidate to revise a specific portion of their presentation based on this feedback.
• Evaluate their ability to adapt their communication style and incorporate feedback.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each exercise is designed to take between 45-90 minutes, depending on the complexity and depth you require. For on-site interviews, we recommend selecting 1-2 exercises that best align with your priorities. The model implementation exercise works well as a take-home assignment if you prefer to save on-site time for discussion and feedback.

Should we use real company data for these exercises?

While using relevant data makes the exercise more authentic, it's not always necessary or appropriate to use actual company data. Consider using anonymized or synthetic data that resembles your production environment, or well-known public datasets relevant to your domain. The key is that the data should present realistic challenges similar to what the candidate would face on the job.

How should we evaluate candidates who use different approaches than we expected?

This is actually a valuable opportunity to learn! Evaluate the candidate's reasoning and the effectiveness of their approach rather than adherence to a specific solution. Strong candidates may introduce novel approaches that your team hasn't considered. Use the discussion portion to understand their decision-making process and the tradeoffs they considered.

What if a candidate doesn't complete the exercise in the allotted time?

Focus on evaluating what they did accomplish and their problem-solving approach rather than completion alone. The exercises are intentionally comprehensive to observe how candidates prioritize under constraints. A candidate who makes thoughtful progress on the most critical aspects may be stronger than one who completes everything with a superficial approach.

How do we ensure these exercises don't disadvantage candidates from diverse backgrounds?

Design exercises that minimize reliance on domain-specific knowledge that isn't core to the role. Provide clear context and documentation. Consider offering flexibility in programming languages or frameworks. Evaluate candidates on their problem-solving approach and learning ability rather than specific tool familiarity. Finally, be consistent in how you administer and evaluate these exercises across all candidates.

Should we share these exercises with candidates before the interview?

For complex technical exercises like the model implementation, providing some information ahead of time can help candidates prepare and reduce interview anxiety. For design and communication exercises, sharing the general format but not the specific scenario maintains the ability to assess real-time thinking while still allowing candidates to prepare effectively.

Machine Learning Engineers are critical to implementing AI solutions that deliver real business value. By incorporating these practical work samples into your hiring process, you'll gain deeper insights into candidates' capabilities than traditional interviews alone can provide. These exercises evaluate not just technical skills, but also the problem-solving approaches, communication abilities, and collaborative mindset that distinguish exceptional ML Engineers.

For more resources to enhance your hiring process, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator. You can also explore our detailed Machine Learning Engineer job description for additional insights into this critical role.

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