Design interviews are probably deciding the level you get assigned to!

Systems design questions (for both software and ML) play a crucial role in determining your level in the industry, whether you’re considered junior, senior, or staff. These questions assess your ability to think holistically about complex systems and demonstrate your industry experience. Here are several key areas that interviewers often focus on:

1. Business and Product Considerations:

   • Understanding how ML solutions impact business metrics
   • Assessing potential cannibalization of revenue or user engagement
   • Aligning ML projects with overall business strategy and goals

2. Model Lifecycle Management:

   • Implementing strategies for detecting and addressing model drift
   • Monitoring and responding to performance drops in production
   • Designing systems for continuous model improvement and updates

3. Data Management and Updates:

   • Strategies for updating and maintaining large-scale datasets
   • Handling updates to embeddings or other pre-computed features
   • Designing efficient data pipelines for real-time and batch processing

4. Work Distribution and Team Collaboration:

   • Architecting systems that allow for efficient distribution of work
   • Designing interfaces between different components or teams
   • Planning for scalability in both technical systems and team structures

5. Business Impact Measurement:

   • Designing experiments to measure the effects of ML systems on business KPIs
   • Implementing A/B testing frameworks for ML models
   • Creating dashboards and reporting systems for stakeholders

6. Evaluation Strategies:

   • Designing comprehensive offline evaluation pipelines
   • Implementing online evaluation systems for real-time performance monitoring
   • Balancing multiple, potentially conflicting metrics

7. Data Quality and Bias Mitigation:

   • Identifying and addressing data quality issues, such as class imbalance or missing data
   • Designing systems to handle data seasonality and temporal effects
   • Implementing strategies to detect and mitigate bias in training data

8. Scalability and Performance Optimization:

   • Designing systems that can handle increasing data volumes and user loads
   • Optimizing model inference for low-latency applications
   • Implementing efficient data storage and retrieval systems

9. Privacy and Security Considerations:

   • Designing systems that comply with data protection regulations (e.g., GDPR, CCPA)
   • Implementing secure model serving infrastructure
   • Handling sensitive data in ML pipelines

10. Cost Optimization:

    • Designing cost-effective ML infrastructure
    • Implementing strategies for efficient resource utilization
    • Balancing model performance with computational costs

Demonstrating depth of knowledge and practical experience in these areas during a design interview can significantly impact the level you’re assigned. It showcases not just your technical skills, but also your ability to navigate the complex landscape of real-world ML system development and deployment.