Essential Work Sample Exercises for Evaluating GPU/TPU Resource Management Skills

GPU and TPU resource management and optimization has become a critical skill in today's AI-driven technology landscape. As organizations invest heavily in machine learning infrastructure, the ability to efficiently allocate, monitor, and optimize computational resources directly impacts both performance outcomes and operational costs. Inefficient resource management can lead to significant bottlenecks, wasted computing power, and unnecessary expenses that can quickly accumulate into millions of dollars for large-scale operations.

Finding candidates who truly understand the nuances of GPU/TPU optimization requires more than reviewing resumes or conducting theoretical interviews. The difference between a candidate who has memorized best practices and one who can implement them effectively in complex, real-world scenarios is substantial. This is where carefully designed work samples become invaluable in the hiring process.

The following exercises are designed to evaluate a candidate's practical skills in managing computational resources across various scenarios they're likely to encounter on the job. These activities assess not only technical knowledge but also problem-solving approaches, communication skills, and the ability to balance competing priorities like performance, cost, and resource availability.

By incorporating these work samples into your interview process, you'll gain deeper insights into how candidates approach resource optimization challenges, their familiarity with common pitfalls, and their ability to make data-driven decisions that align with business objectives. Additionally, you'll observe how they respond to feedback and adapt their solutions accordingly—a crucial skill in the rapidly evolving field of accelerated computing.

Activity #1: Multi-Team Resource Allocation Strategy

This exercise evaluates a candidate's ability to design a comprehensive resource allocation strategy for a complex ML environment with multiple teams and competing priorities. It tests their understanding of GPU/TPU architecture, workload characteristics, and organizational decision-making. This skill is essential as it directly impacts both team productivity and infrastructure costs.

Directions for the Company:

• Prepare a scenario document describing a fictional organization with 3-4 different ML teams (e.g., computer vision, NLP, recommendation systems) with different workload patterns and priorities.
• Include details about available hardware (e.g., 20 NVIDIA A100 GPUs, 8 TPU v4 pods), current allocation methods, and business constraints.
• Provide utilization metrics showing current inefficiencies (e.g., some teams experiencing resource starvation while others have idle resources).
• Include a budget constraint and information about cloud vs. on-premise resources.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the scenario document and analyze the current resource allocation approach.
• Design a resource allocation strategy that optimizes for both team productivity and cost efficiency.
• Create a document or diagram outlining:
• Proposed allocation methodology (e.g., fair-share scheduling, priority-based, dynamic allocation)
• Technical implementation details (specific tools, configurations)
• Monitoring approach to ensure ongoing optimization
• Governance process for handling priority conflicts
• Be prepared to present and defend your strategy, explaining the tradeoffs you considered.

Feedback Mechanism:

• After the candidate presents their strategy, provide feedback on one strength (e.g., "Your approach to dynamic allocation based on project deadlines was well-thought-out") and one area for improvement (e.g., "Consider how you might better address the needs of exploratory research workloads").
• Ask the candidate to revise one specific aspect of their strategy based on the feedback, giving them 10 minutes to make adjustments.
• Observe how they incorporate the feedback and whether they can adapt their thinking.

Activity #2: ML Model Performance Optimization

This exercise tests a candidate's hands-on ability to identify and resolve performance bottlenecks in GPU/TPU workloads. It evaluates their proficiency with profiling tools, optimization techniques, and their systematic approach to performance tuning—skills that directly translate to reducing training time and infrastructure costs.

Directions for the Company:

• Prepare a simple but inefficient ML model implementation (in PyTorch, TensorFlow, or JAX) that contains common performance issues such as:
• Suboptimal batch size
• CPU bottlenecks in the data pipeline
• Inefficient memory usage
• Mixed precision not being utilized
• Provide access to a development environment with appropriate GPU/TPU resources and profiling tools.
• Include baseline performance metrics for the model.
• Allow 60-90 minutes for this exercise.

Directions for the Candidate:

• Review the provided model implementation and baseline performance metrics.
• Use appropriate profiling tools to identify performance bottlenecks.
• Implement optimizations to improve training/inference throughput.
• Document each optimization applied, the rationale behind it, and the resulting performance impact.
• Aim to achieve at least a 2x performance improvement through your optimizations.
• Prepare a brief summary of your approach and findings, including any additional optimizations you would implement given more time.

Feedback Mechanism:

• Review the candidate's optimizations and provide feedback on one effective approach they took (e.g., "Your data pipeline optimization significantly reduced CPU bottlenecks") and one missed opportunity (e.g., "You could further improve performance by implementing gradient accumulation").
• Ask the candidate to implement one additional optimization based on your feedback, giving them 15-20 minutes.
• Evaluate both their technical implementation and their explanation of the expected performance impact.

Activity #3: GPU Memory Issue Debugging

This exercise assesses a candidate's troubleshooting skills and deep understanding of GPU memory management. The ability to diagnose and resolve memory-related issues is crucial for maintaining stable ML infrastructure and preventing costly training failures in production environments.

Directions for the Company:

• Create a problematic ML script that exhibits common GPU memory issues such as:
• Out-of-memory errors with large models
• Memory fragmentation
• Memory leaks in custom operations
• Unnecessary tensor retention
• Include error logs and partial profiling output that provide clues but don't explicitly reveal all issues.
• Provide access to a development environment with appropriate debugging tools.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the provided script, error logs, and profiling information.
• Identify the root causes of the memory issues.
• Implement fixes to resolve the memory problems while maintaining model accuracy.
• Document your diagnostic process, including:
• Tools and commands used to investigate the issues
• Each problem identified and its root cause
• The solution implemented and its rationale
• Any trade-offs made in your approach
• Successfully run the model without memory errors.

Feedback Mechanism:

• After the candidate presents their solutions, provide feedback on one effective debugging technique they employed (e.g., "Your systematic use of memory profiling to identify tensor accumulation was excellent") and one area where their approach could be improved (e.g., "Consider how gradient checkpointing could address the memory constraints more elegantly").
• Ask the candidate to implement or explain an alternative approach to one of the issues based on your feedback, giving them 10-15 minutes.
• Evaluate their flexibility in considering alternative solutions and their understanding of the underlying memory management principles.

Activity #4: Cost-Efficiency Analysis and Optimization

This exercise evaluates a candidate's ability to balance performance requirements with cost considerations—a critical skill as organizations scale their ML infrastructure. It tests their knowledge of different hardware options, pricing models, and optimization strategies from a business perspective.

Directions for the Company:

• Prepare a scenario document describing a growing ML workload with:
• Current infrastructure setup (on-premise and/or cloud)
• Monthly cost breakdown
• Performance requirements for different workloads
• Growth projections for the next 12 months
• Include pricing information for various GPU/TPU options (both purchase and cloud rental).
• Provide performance benchmarks for the workloads on different hardware.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Analyze the current infrastructure costs and performance metrics.
• Develop a recommendation for optimizing the infrastructure to reduce costs while meeting or exceeding performance requirements.
• Your recommendation should include:
• Proposed hardware changes (if any)
• Cloud vs. on-premise strategy
• Specific optimization techniques to improve resource utilization
• Cost-benefit analysis with projected savings
• Implementation timeline and migration strategy
• Consider both immediate optimizations and long-term scalability.
• Prepare a brief presentation of your recommendations suitable for both technical and business stakeholders.

Feedback Mechanism:

• After the candidate presents their recommendations, provide feedback on one strength of their analysis (e.g., "Your hybrid approach leveraging spot instances for non-critical workloads shows good cost awareness") and one area for deeper consideration (e.g., "Your analysis should factor in the operational overhead of managing multiple instance types").
• Ask the candidate to refine one aspect of their recommendation based on the feedback, giving them 15 minutes.
• Evaluate how they incorporate business considerations into their technical recommendations and their ability to communicate complex trade-offs clearly.

Frequently Asked Questions

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

Each exercise is designed to take 45-90 minutes, depending on complexity. For a comprehensive assessment, we recommend selecting 1-2 exercises that best align with your specific needs rather than attempting all four. The exercises can be conducted during an onsite interview or as a take-home assignment with a follow-up discussion.

Should candidates be allowed to use reference materials or the internet during these exercises?

Yes, allowing access to documentation, Stack Overflow, and other resources more accurately reflects real-world working conditions. However, you may want to ask candidates to explain their research process and cite any sources they used, which provides insight into their problem-solving approach.

How should we evaluate candidates who use different tools or approaches than we currently use?

Focus on the principles and reasoning behind their choices rather than specific tool familiarity. A candidate who demonstrates strong fundamentals in a different framework can often transfer those skills. Their unique perspective might even introduce valuable new approaches to your team.

What if we don't have the infrastructure to support these exercises?

For companies without extensive GPU/TPU resources, consider:

• Using smaller-scale versions of the exercises on less powerful hardware
• Leveraging cloud credits for interview environments
• Converting some exercises to discussion-based scenarios where candidates explain their approach
• Using paper designs for the planning exercises

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

Provide clear instructions and sufficient context for all candidates. Consider offering a brief pre-exercise orientation to familiarize candidates with your specific environment. Evaluate candidates based on their problem-solving approach and communication as much as their technical solution, recognizing there are multiple valid approaches to these challenges.

Should we share these exercises with candidates in advance?

For complex exercises, providing some information in advance (such as the general topic or scenario) can help candidates prepare and reduce interview anxiety. However, specific details should be reserved for the actual exercise to ensure you're evaluating real-time problem-solving abilities.

Effective GPU/TPU resource management can be the difference between an AI initiative that delivers breakthrough results and one that drains resources without proportional returns. By incorporating these practical work samples into your hiring process, you'll be better equipped to identify candidates who can truly optimize your computational resources, balance competing priorities, and drive both technical excellence and business value.

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