Essential Work Sample Exercises for Hiring AI Compute Cluster Managers

AI compute cluster management has become a critical function for organizations leveraging artificial intelligence and machine learning at scale. As AI workloads grow in complexity and resource demands, the expertise required to efficiently manage these specialized computing environments has become increasingly valuable. Hiring the right talent for these roles directly impacts an organization's ability to optimize AI infrastructure costs, maintain performance, and support innovation.

The technical complexity of AI compute environments—spanning GPU allocation, network architecture, storage optimization, and workload scheduling—requires candidates with both breadth and depth of knowledge. Traditional interviews often fail to reveal a candidate's true capabilities in these areas, as theoretical knowledge doesn't always translate to practical problem-solving skills.

Work sample exercises provide a window into how candidates approach real-world challenges they'll face on the job. By observing candidates tackle representative tasks, hiring teams can evaluate technical competence, problem-solving approaches, communication skills, and adaptability—all critical for success in AI infrastructure roles.

The following four exercises are designed to assess key competencies for AI compute cluster management positions. Each activity simulates realistic scenarios that cluster managers encounter, allowing candidates to demonstrate their expertise while giving hiring teams concrete evidence of capabilities. By incorporating these exercises into your interview process, you'll gain deeper insights into which candidates can truly excel at managing your AI compute infrastructure.

Activity #1: AI Cluster Scaling Plan

This exercise evaluates a candidate's ability to plan for growth and scale AI infrastructure strategically. Effective cluster managers must balance immediate needs with future requirements, considering technical constraints, cost implications, and implementation timelines. This activity reveals how candidates approach complex planning challenges and their understanding of AI workload characteristics.

Directions for the Company:

• Provide the candidate with a scenario document describing your current AI compute infrastructure (e.g., 20 nodes with 8 GPUs each, network topology, storage configuration) and projected growth in AI workloads over the next 12 months.
• Include details about current utilization metrics, bottlenecks, and budget constraints.
• Prepare a template document where candidates can outline their scaling plan.
• Allow 45-60 minutes for this exercise.
• Have a technical leader from your AI infrastructure team available to evaluate the plan and provide feedback.

Directions for the Candidate:

• Review the provided infrastructure details and growth projections.
• Create a comprehensive scaling plan that addresses:
• Hardware additions/upgrades (compute, storage, networking)
• Architecture changes to accommodate growth
• Implementation phases with timeline estimates
• Cost projections and ROI considerations
• Potential risks and mitigation strategies
• Be prepared to explain your rationale for key decisions in the plan.
• Consider both technical and business constraints in your approach.

Feedback Mechanism:

• The interviewer should provide feedback on two aspects: a strength of the scaling plan (e.g., "Your phased approach to GPU expansion aligns well with our workload growth pattern") and an area for improvement (e.g., "The plan could better address network bottlenecks that might emerge").
• Give the candidate 10 minutes to verbally explain how they would revise their approach based on the improvement feedback.
• Assess both the quality of the initial plan and the candidate's adaptability in incorporating feedback.

Activity #2: AI Cluster Performance Troubleshooting

This exercise assesses a candidate's ability to diagnose and resolve performance issues in an AI compute environment. Troubleshooting is a daily responsibility for cluster managers, requiring systematic analysis, technical knowledge across multiple domains, and effective communication. This activity reveals how candidates approach complex problems under time constraints.

Directions for the Company:

• Create a realistic scenario describing performance degradation in an AI training job (e.g., "A previously stable training job that typically completes in 8 hours is now taking 14+ hours").
• Provide system logs, monitoring dashboards, and configuration details that contain clues to the underlying issues.
• Include some red herrings to test the candidate's focus on relevant information.
• Prepare a document with 3-4 potential issues embedded in the materials (e.g., GPU throttling due to thermal issues, network contention, storage I/O bottlenecks).
• Allow 30-45 minutes for this exercise.

Directions for the Candidate:

• Review the provided logs, metrics, and configuration information.
• Identify potential causes of the performance degradation.
• Document your troubleshooting approach, including:
• What metrics or logs you analyzed first and why
• The potential issues you identified
• How you would prioritize investigating these issues
• Recommended immediate actions to mitigate the problem
• Long-term solutions to prevent recurrence
• Be prepared to explain your reasoning and methodology.

Feedback Mechanism:

• The interviewer should highlight one effective aspect of the candidate's troubleshooting approach and one area where their analysis could be improved or expanded.
• Ask the candidate to spend 5-10 minutes explaining how they would adjust their approach based on the feedback.
• Evaluate both technical accuracy in identifying issues and the structured nature of their troubleshooting methodology.

Activity #3: AI Compute Cost Optimization Challenge

This exercise evaluates a candidate's ability to optimize costs while maintaining performance in AI compute environments. As AI infrastructure costs can quickly escalate, effective cluster managers must identify efficiency opportunities across hardware, software, and operational dimensions. This activity reveals a candidate's business acumen alongside their technical knowledge.

Directions for the Company:

• Prepare a detailed cost breakdown of your current AI compute infrastructure, including:
• Hardware costs (servers, GPUs, networking equipment)
• Cloud or data center expenses
• Software licensing
• Power and cooling costs
• Support and maintenance contracts
• Include utilization metrics showing patterns of usage across different teams and projects.
• Provide information about current scheduling policies, resource allocation methods, and governance structures.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the provided cost and utilization information.
• Identify at least 5 specific opportunities to reduce costs without significantly impacting performance or availability.
• For each opportunity, provide:
• Estimated cost savings (percentage or dollar amount)
• Implementation complexity (high/medium/low)
• Potential risks or trade-offs
• Timeline for realizing savings
• Prioritize your recommendations based on impact, feasibility, and risk.
• Be prepared to defend your recommendations with technical and business rationales.

Feedback Mechanism:

• The interviewer should acknowledge one particularly valuable cost optimization suggestion and identify one recommendation that might have unintended consequences or implementation challenges.
• Ask the candidate to spend 10 minutes refining their approach to the challenged recommendation, addressing the concerns raised.
• Evaluate the candidate's balance of technical understanding, business impact awareness, and adaptability to feedback.

Activity #4: Automated Resource Management Implementation

This exercise assesses a candidate's ability to implement automation for AI cluster resource management. Automation is essential for efficiently managing complex AI infrastructure at scale, reducing manual intervention, and ensuring consistent resource allocation. This activity reveals a candidate's technical implementation skills and understanding of automation principles.

Directions for the Company:

• Prepare a scenario describing a specific automation need (e.g., "Implement a solution that automatically scales GPU resources based on job queue depth and priority").
• Provide relevant details about your environment, including:
• Current resource management tools and APIs
• Job scheduling system
• Monitoring infrastructure
• Any existing automation scripts or tools
• Specify whether you want candidates to write actual code or pseudocode.
• If coding is required, specify acceptable languages (Python is typically appropriate).
• Allow 60 minutes for this exercise.

Directions for the Candidate:

• Design and implement (or describe in detail) an automation solution that addresses the specified need.
• Your solution should include:
• Overall architecture and components
• Logic for decision-making (when and how to scale resources)
• Integration points with existing systems
• Error handling and fallback mechanisms
• Monitoring and alerting considerations
• If writing code, focus on clarity and structure rather than syntactic perfection.
• Be prepared to explain your design choices and implementation details.
• Consider both technical functionality and operational reliability in your solution.

Feedback Mechanism:

• The interviewer should highlight one strong aspect of the automation solution and one area that could be enhanced or might present operational challenges.
• Give the candidate 15 minutes to revise a specific portion of their solution based on the feedback.
• Evaluate both the technical quality of the initial solution and the candidate's ability to incorporate feedback effectively.

Frequently Asked Questions

How long should we allocate for these work sample exercises?

Each exercise is designed to take 30-60 minutes, depending on complexity. For remote assessments, you might send one exercise as a take-home assignment with a 2-3 hour time limit. For on-site interviews, select 1-2 exercises that best align with your specific needs. The entire battery would be too time-consuming for a single interview session.

Should candidates have access to reference materials during these exercises?

Yes, allowing access to documentation and reference materials creates a more realistic work environment. AI compute cluster managers regularly consult documentation, especially for specific syntax or parameters. This approach tests problem-solving ability rather than memorization. However, be clear about what resources are permitted (e.g., public documentation vs. asking AI assistants to solve the problem).

How technical should the interviewer be to evaluate these exercises?

The evaluator should have sufficient technical knowledge of AI infrastructure to assess the quality of solutions. Ideally, the evaluator should be someone who works directly with AI compute resources, such as a senior infrastructure engineer, DevOps lead, or current cluster manager. For companies without this expertise in-house, consider bringing in a technical consultant to assist with evaluation.

Can these exercises be adapted for different levels of seniority?

Absolutely. For junior roles, simplify the scenarios and provide more structure in the requirements. For senior or leadership positions, add complexity such as multi-region considerations, integration with business processes, or strategic planning elements. The core activities remain valuable across levels, but the expected depth and breadth of solutions should align with the role's seniority.

How should we weight these exercises compared to traditional interviews?

Work samples should comprise 40-60% of your overall evaluation, with traditional interviews covering cultural fit, team dynamics, and broader experience. These exercises provide concrete evidence of capabilities that are difficult to assess through conversation alone. However, they should complement rather than replace discussions about past experiences and approaches to collaboration.

Should we share these exercises with candidates in advance?

For complex exercises like the scaling plan or cost optimization challenge, providing the scenario 24-48 hours in advance can yield more thoughtful responses. For troubleshooting or implementation exercises that test real-time problem-solving, surprise scenarios are more appropriate. Be transparent with candidates about which portions will be provided in advance versus during the interview.

Implementing these work sample exercises will significantly enhance your ability to identify candidates with the right combination of technical knowledge, problem-solving skills, and business acumen needed for effective AI compute cluster management. By observing candidates tackle representative challenges, you'll gain insights that traditional interviews simply cannot provide.

For more resources to improve your hiring process, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator. These tools can help you build a comprehensive, effective hiring process for technical roles like AI compute cluster management.

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