AI Palette interview prep, start to offer.

Discussion of your background, research interests, and the role. ML roles often include a quick technical discussion about your past work.

• Be prepared to discuss your papers or projects in detail
• Know the company's recent publications or product launches
• Ask about the team's current research direction

Coding plus ML concepts. May include implementing an ML algorithm from scratch or discussing model architecture choices.

• Know how to implement common algorithms: logistic regression, decision trees, k-means
• Brush up on gradient descent, backpropagation, and loss functions
• Be comfortable coding in Python with NumPy

Extended discussion of ML concepts, model evaluation, feature engineering, and production ML challenges.

• Know bias-variance tradeoff, overfitting mitigation, and model selection
• Be ready to discuss model monitoring and data drift
• Understand the full ML lifecycle, not just training

Design an ML system end-to-end: data pipeline, feature store, model training, serving, and monitoring.

• Practice designing recommendation systems, search ranking, and fraud detection
• Discuss feature engineering, online vs. offline serving
• Consider model freshness, A/B testing, and rollback strategies

Some companies ask you to present your research or discuss a recent paper. Tests depth of understanding and ability to communicate complex ideas.

• Choose a paper you understand deeply, not just superficially
• Be ready to discuss limitations and potential improvements
• Practice explaining technical concepts to different audiences