Snapchat Technical Screening Prep

Preparing for Snapchat Technical Screening

• Blends software engineering rigor, machine learning expertise, and data intuition.
• Assesses coding skills, ML fundamentals, applied ML design, and ML system design.

Key areas of focus

• Coding: Requires solving algorithmic problems with clean, efficient code using appropriate data structures and algorithms.
• ML Fundamentals: Assesses discussion on core ML including supervised vs unsupervised learning, recommendation systems, ranking algorithms, model evaluation metrics, and optimization techniques.
• Applied ML Design: Requires end-to-end solutions to real-world problems, including model selection, feature engineering, and performance evaluation in ambiguous settings.
• ML System Design: Requires designing scalable, robust systems for production, addressing large-scale data, deployment, infrastructure trade-offs, and monitoring.
• For Technical Program Manager (TPM) or ML platform role, emphasis is on demonstrating software engineering, familiarity with data science, data, and ML Ops knowledge.
• Important to effectively communicate with engineering teams and drive projects.

Machine Learning Fundamentals

• Algorithms learn patterns from data to make predictions or decisions.

Supervised vs. Unsupervised Learning

• Supervised learning involves models that learn from labeled input-output pairs to predict outputs for new inputs, such as image classification or number prediction.
• Unsupervised learning involves finding patterns or groupings in unlabeled data, such as clustering users by behavior.

Model Training and Evaluation

• Training involves optimizing a model's parameters on a dataset with evaluation metrics like accuracy, precision/recall and Root Mean Squared Error (RMSE).
• Good practice involves splitting data into training, validation, and test sets to detect overfitting, which occurs when a model memorizes training data but performs poorly on new data.
• Techniques such as cross-validation and regularization, like dropout in neural nets, can combat overfitting.

Feature Engineering

• Transforms raw data into input features ML algorithms can work with, for instance, converting timestamps into day-of-week, extracting word frequencies, or normalizing numerical ranges.
• Representation learning can automate this in modern ML, especially deep learning

Common Algorithms

• Includes linear models like linear or logistic regression, decision trees/ensembles (random forests, gradient boosting), and basic neural networks.
• Decision trees handle heterogeneous features and rank feature importance while neural networks handle large data and complex patterns

Model Tuning

• Tuning model performance using optimization techniques like grid search, random search, or Bayesian optimization to adjust hyperparameters such as learning rate, tree depth, and regularization strength.
• Bayesian optimization suggests values based on past evaluation results.

MLOps Concepts and Industry Trends

• MLOps combines Machine Learning (ML), software engineering (DevOps), and data engineering to reliably deploy and maintain ML models in production.
• MLOps focuses its automation, versioning, testing, and monitoring across the ML lifecycle and includes reproducible workflows and CI/CD.
• Key components include reproducible workflows, Continuous Integration/Continuous Deployment (CI/CD), Model Deployment & Serving, Monitoring and Model Performance, and Collaboration and Lifecycle Management

Reproducible Workflows

• Involves source control for code and data, environment management, and experiment tracking, like using Git for code and tracking datasets/models with version IDs or model registry.

Continuous Integration Continuous Deployment CI/CD

• Uses automated pipelines to train, test, and deploy models to ensure new versions are validated and rolled out.
• By 2025, companies that adopt MLOps could achieve a 30–50% reduction in model deployment time due to automation.

Model Deployment & Serving

• Packaging models as microservices or via cloud ML platforms for scalable predictions through batch or real-time APIs.
• Tools such as Docker/Kubernetes, AWS SageMaker, and Google Vertex AI are used.
• Inference architecture has to include scalability (caching) and latency or GPUs for heavy deep learning models.

Monitoring and Modern Performance

• After deployment, models must be monitored for data distribution shifts and performance degradation.
• MLOps requires logging predictions and actuals to detect anomalies, triggering alerts for retraining or fallback.

Collaboration and Lifecycle Management

• Promotes data sharing between data scientists, engineers, and stakeholders with tools feature stores and dashboards to track metrics.
• Enforces governance, reproducibility, audit trails, and regulatory compliance.

Industry Trends

• The global MLOps market will reach $16.6 billion by 2030, at nearly 40% annual growth, and will be streamlined using internal ML platforms.
• Uber has built Michelangelo, a ML-as-a-service platform that covers end-to-end workflows.
• Google is deploying TensorFlow Extended (TFX) to reduce time-to-production for ML models by providing components for ingestion, validation, training and serving.
• Productionizing ML at scale like Snap's Bento focuses not just on accurate models, but reliable operationalization.
• Implementing MLOps would build a data pipeline to aggregate user interactions, weekly retraining model, API deploying the new model and dip monitoring or change the alert.

Software Engineering Fundamentals

Expect interview questions around system design and coding best practices to build on solid architecture.

• System Design Principles, Scalability, Reliability & Redundancy, Consistency vs Availability and Loose Coupling & High Cohesion
• Microservices Architecture
• Software Engineering Best Practices

System Design Principles

• Involves planning software architecture to meet scalability, reliability, and maintainability requirements.

Scalability

• Design infrastructure for graceful load management (horizontal scaling) behind load balancers, and caching data to reduce load on databases.
• Use Content Delivery Networks (CDNs) for static content and partitioning (e.g., sharding a database by user region)

Reliability & Redundancy

• Guarantee no single points of failure, (multiple availability zones, master-slave or multi-primary databases), and graceful degradation

Consistency vs availablity

• Need for trade offs, CAP theorem for distributed systems

Loose Coupling & High Cohesion

• Using clear responsibilities, and minimal internal awareness for service modules enables easier modification, modification and less unexpected errors

Microservices Architecture

• Application structure has single-capability small services independently for typically loosely coupled projects

Benefits

• Able to evolve platform scaling separately(friend recommendations does not affect messages) and using the best tech per service(Python for smaller ML, Go for larger network

Be practices for microservices

• Define clear, versioned interfaces (RESTful APIs or gRPC contracts) for service communication.
• Implement service discovery and load balancing
• Use consistent observability practices for metrics output e.g. Promethus
• Gracefully handle service malfunctions instead of hanging indefinitely

Software Engineering Best Practices

• Clean code and Modularity: Write code that is readable and modular. Use meaningful naming style
• Version Control Code Reviews: Use Git for collaborative pull requests - Tip:Provide more context for changes
• Testing Emphasize testing to ensure pipeline stability and correctness
• Performance and Optimization: Use profiling tools to find bottlenecks when optimizing
• Documentation: Maintain documentation for team clarity.

Data Science Fundamentals

• Including SQL, data analysis and visualization, notebooks, code review for analytics code, and general analytics/experimentation knowledge.
• SQL and Data Querying, Dashboarding and Visualization, Python Notebooks and Analysis, Code Reviews and Collaboration(PR Reviews)
• Analysis and Experimentation

SQL and Data Querying

• SQL (Structured Query Language) allows comfortable select, filter, and data aggregation through query.
• Learn different JOIN types, GROUP BY and HAVING functions.

Optimization

• Appropriate indexes on columns is vital for query speed, discuss examined query execution plan

Dashboarding and Visualization

• Involves creating interactive charts with metrics like active daily users that have clear visualization and real time anomaly highlight

Key tips to follow

• Match audience detail to dashboard, pick proper data chart type, keep it simple.
• Mention familiar tools for reports, and follow consistent metric definition

Python Notebooks Analysis

• Jupyter Notebook interactive environments are standard for data scientists.
• Provides exploratory data analysis and prototyping models.
• Usage loading data, cleaning, training small scale models, and plotting outcomes with shareable results

Best Practices For Analysis

• Keep notebooks organized with proper cell sections to restart data with a clean state

Code Reviews and Collaboration(PR Reviews)

• Important to help maintain SQL analytics/ml with best practices
• Context Clarity is required - include context that helps reviewer understand the intent of the change/project Style and Maintainability - Maintain team style (naming andformatting)
• Performance should be considered to optimize.

SQL Review

• The reviewer looks for indexes that might be added

Data Science

• Review covers methodology and code as approaching a high data output project

Analytics and Experimentation

• Data driven initiatives with knowledge of product metrics and key products
• KPI definitions are important
• Test common product change with ML updates
• A\B test - splitting users into parts variants while maintaining control
• Data Analysis - data driven and data metrics from multiple sources
• Need to present results with Python

Database fundamentals

• Efficiently retrieving data from ML platform context, deal metadata or standards from product databases include data, query, and transaction

Data management includes:

• Data modeling to structure and secure data relationally