AI and Algorithm Interview Core Topics: 7 Modules from Machine Learning to Large Models

AI and Algorithm Interview Core Topics: 7 Modules from Machine Learning to Large Models

Competition in AI interviews is fiercer than ever, with topics spanning from traditional machine learning to large model fine-tuning. Many candidates excel in one area but fall short in others. This article systematically breaks down 7 core modules for AI algorithm interviews, each with high-frequency topics and answer frameworks to help you identify gaps and prepare efficiently.

1. Mathematical Foundations: The Bedrock of AI Interviews

Math is the underlying support for AI interviews. Interviewers often use math questions to assess your depth of thinking and derivation ability. Without the ability to derive formulas, it's hard to go far in algorithm interviews.

1.1 Linear Algebra

Linear algebra is the cornerstone for understanding forward and backward propagation in deep learning. High-frequency topics focus on matrix operations and decomposition.

• Eigenvalues and Eigenvectors: Understand geometric meaning (direction unchanged under transformation), master power iteration method
• SVD Decomposition: Master derivation of A=UΣVᵀ, understand applications in dimensionality reduction and recommendation systems
• Matrix Calculus: Scalar-to-vector derivatives, scalar-to-matrix derivatives, master chain rule
• Positive Definite Matrices: Definition, determination methods, significance in optimization (positive definite Hessian → local minimum)

1.2 Probability and Statistics

Probability and statistics are the language of machine learning modeling. Bayesian thinking permeates the entire AI field.

• Bayes' Theorem: Relationship between prior, likelihood, and posterior; applications in Naive Bayes and Bayesian optimization
• Common Distributions: Properties and connections of normal, Poisson, and exponential family distributions
• MLE vs MAP: Derivation process, connections and differences
• Hypothesis Testing: Meaning of p-value, Type I/Type II errors, applications in A/B testing

1.3 Optimization Theory

Optimization is the core engine of model training. Interviews frequently test convex optimization basics and gradient descent variants.

• Convex Function Determination: Positive semi-definite Hessian → convex function, master common convex function examples
• Gradient Descent Variants: Principles and pros/cons comparison of SGD, Momentum, and Adam
• Lagrange Multipliers: Derivation for equality constraints and inequality constraints (KKT conditions)
• Learning Rate Scheduling: Applicable scenarios for Warmup, Cosine Annealing, and StepLR

1.4 Math Module Answer Strategy

1. Start with intuitive explanation: Explain the geometric or physical meaning in one sentence
2. Then write mathematical derivation: Derive step by step from definition; key steps cannot be skipped
3. Connect to practical applications: Explain where this mathematical tool plays a role in algorithms or models

2. Traditional Machine Learning: The Fundamentals of AI Interviews

Despite the deep learning hype, traditional ML remains a must-test area in AI interviews. Interviewers use traditional ML questions to assess your modeling thinking and theoretical foundation, which separates "library users" from those who truly understand algorithms.

2.1 Support Vector Machines (SVM)

• Core Idea: Maximize classification margin; decision boundary determined only by support vectors
• Dual Problem Derivation: Primal problem → Lagrangian function → KKT conditions → Dual problem
• Kernel Functions: Principles and selection strategies for RBF and polynomial kernels; kernel trick avoids explicit mapping
• Soft Margin and C Parameter: Larger C = less tolerance for misclassification; smaller C = more emphasis on generalization

2.2 Tree Models and Ensemble Learning

• Decision Trees: Splitting criteria for ID3 (information gain), C4.5 (gain ratio), and CART (Gini index)
• Random Forest: Bagging + random feature sampling, reduces variance, OOB evaluation
• GBDT: Forward stagewise additive model, each tree fits negative gradient (residual), reduces bias
• XGBoost vs LightGBM: XGBoost grows by level, LightGBM grows by leaf; LightGBM uses histogram acceleration and GOSS downsampling

2.3 Traditional ML High-Frequency Topics

• Bias-Variance Tradeoff: Bagging reduces variance, Boosting reduces bias
• Overfitting Prevention: L1/L2 regularization, early stopping, cross-validation, data augmentation
• Feature Engineering: Missing value handling, encoding methods (One-Hot/Target/Embedding), feature selection
• Evaluation Metrics: Applicable scenarios for Precision/Recall/F1/AUC; AUC's robustness to class imbalance

2.4 Traditional ML Answer Strategy

1. One-sentence algorithm summary: Give the interviewer a clear overall impression first
2. Core derivation or key steps: Show you understand the algorithm's internal mechanisms
3. Pros and cons comparison: Compare horizontally with similar algorithms; explain applicable scenarios
4. Practical project experience: Combine with projects you've done; explain selection rationale and tuning process

3. Deep Learning Fundamentals: The Core Battlefield of AI Interviews

Deep learning is the most critical part of AI algorithm interviews. Interviewers expect you to not only use frameworks but also explain network architectures from first principles. From CNN to Transformer, each architecture has clear design motivations.

3.1 Convolutional Neural Networks (CNN)

• Convolution Operations: Receptive field calculation, multi-channel convolution, role of 1×1 convolution (dimensionality reduction/increase/cross-channel fusion)
• Pooling Layers: Max pooling preserves salient features; average pooling preserves global information
• Classic Architecture Evolution: ResNet (residual connections solve degradation), Inception (multi-scale features), EfficientNet (compound scaling)
• Deconvolution and Transposed Convolution: Upsampling role in semantic segmentation and image generation

3.2 RNN and Sequence Models

• RNN Gradient Issues: Causes of gradient vanishing/exploding, BPTT derivation
• LSTM: Mechanisms of forget gate, input gate, and output gate; information flow in cell state
• GRU: Reset gate and update gate; fewer parameters than LSTM
• Bidirectional and Multi-layer RNN: Applicable scenarios and computational overhead

3.3 Transformer

Transformer is the highest-frequency topic in current AI interviews. Make sure to deeply understand every component.

• Self-Attention Mechanism: Source and computation of Q/K/V, mathematical expression of scaled dot-product attention
• Multi-Head Attention: Significance of multiple heads (different subspaces capture different relationships), head count selection
• Positional Encoding: Derivation of sinusoidal positional encoding, principles of Rotary Position Embedding (RoPE)
• Layer Normalization: Training stability differences between Pre-Norm and Post-Norm
• FFN Layer: Two linear transformations + activation function; role of increasing then decreasing dimensionality

3.4 Deep Learning Answer Strategy

1. Architecture design motivation: Why this design? What problem does it solve compared to previous architectures?
2. Key formula writing: Be able to write attention formula, residual connections, and normalization formulas on the spot
3. Training techniques: Role of BatchNorm/LayerNorm, learning rate scheduling, gradient clipping
4. Business integration: Explain how to choose and adjust network structures in specific projects

4. NLP and CV Specializations: Domain Depth in AI Interviews

AI algorithm positions typically require deep understanding in either NLP or CV. Interviewers will probe deeply into your area of specialization to assess whether you've actually done projects, not just studied theory.

4.1 NLP High-Frequency Topics

• Word Vectors: Principles and comparison of Word2Vec (CBOW/Skip-gram), GloVe, and FastText
• Pre-trained Language Models: BERT (MLM+NSP), GPT series (autoregressive), T5 (Encoder-Decoder)
• Text Classification: TextCNN, HAN, BERT fine-tuning classification head design
• Sequence Labeling: Role of CRF layer, BIO tagging scheme, NER solutions
• Text Generation: Beam Search, sampling strategies, repetition penalty mechanisms

4.2 CV High-Frequency Topics

• Object Detection: Two-stage (Faster R-CNN) vs one-stage (YOLO series), Anchor-based vs Anchor-free
• Semantic Segmentation: FCN, U-Net, DeepLab series (dilated convolution/ASPP)
• Image Generation: GAN training stability, Diffusion Model forward/reverse processes
• Multi-Modal: CLIP contrastive learning, BLIP image-text alignment, Stable Diffusion architecture
• Data Augmentation: Effects of CutMix, MixUp, and Mosaic in detection tasks

4.3 NLP/CV Answer Strategy

1. Clear task definition: First explain what the task is and what the inputs/outputs are
2. Technical solution evolution: Evolution path from baseline to SOTA, motivation for each improvement
3. Core loss functions: Applicable scenarios for cross-entropy, Focal Loss, and Dice Loss
4. Metrics and evaluation: Calculation methods for BLEU/ROUGE (NLP) and mAP/IoU (CV)

5. Large Models and LLMs: The Frontier of AI Interviews

Large models are the hottest topic in current AI interviews. Almost all algorithm position interviews involve LLM-related questions. From pre-training to fine-tuning to alignment, you need a complete knowledge system.

5.1 Pre-training

• Data Engineering: Data cleaning pipeline, deduplication strategies (MinHash/SimHash), data mixing ratios
• Training Strategies: Causal Language Modeling (CLM), Masked Language Modeling (MLM), Flash Attention acceleration
• Scaling Laws: Chinchilla laws, optimal ratio of compute, data volume, and model scale
• Long Context: RoPE extrapolation, NTK-aware scaling, YaRN principles

5.2 Fine-tuning

• Full Fine-tuning: All parameters updated; best results but high resource consumption
• LoRA: Low-rank decomposition W=W₀+BA, only train B and A; parameter count reduced by 1000x
• QLoRA: 4-bit quantization + LoRA; fine-tune large models on consumer-grade GPUs
• Prefix Tuning / P-Tuning v2: Add trainable prefixes to each layer; suitable for generation tasks

5.3 RLHF and Alignment

• RLHF Pipeline: SFT → Reward Model training → PPO reinforcement learning alignment
• DPO: Direct Preference Optimization; bypasses Reward Model, simplifies alignment pipeline
• Constitutional AI: Guiding model self-correction through principles
• Safety Alignment: Red teaming, jailbreak attack/defense, harmful content filtering

5.4 Prompt Engineering

• Basic Techniques: Zero-shot, Few-shot, Chain-of-Thought (CoT)
• Advanced Techniques: Self-Consistency, Tree-of-Thought, ReAct framework
• System Prompt Design: Role setting, output format constraints, safety boundaries
• RAG (Retrieval-Augmented Generation): Vector retrieval + LLM generation; solves hallucination and knowledge timeliness

5.5 Large Model Answer Strategy

1. Macro to micro: First explain the overall training pipeline, then dive into technical details of each stage
2. Comparative analysis: Pros and cons of LoRA vs Full FT, RLHF vs DPO
3. Practical experience: Describe models you've fine-tuned, pitfalls encountered, and tuning strategies
4. Frontier awareness: Stay updated on latest papers (e.g., GRM, KAN), demonstrate academic sensitivity

6. Engineering and Deployment: Production Capability in AI Interviews

Algorithm engineers are not researchers. Getting models into production is the ultimate goal. Interviewers increasingly value engineering skills, assessing whether you can move models from notebooks to production environments.

6.1 Model Compression

• Quantization: PTQ (Post-Training Quantization) and QAT (Quantization-Aware Training); accuracy loss and compensation for INT8/INT4
• Pruning: Structured pruning (entire channel/layer) vs unstructured pruning (sparsification); Lottery Ticket Hypothesis
• Knowledge Distillation: Teacher-student framework, feature distillation vs logits distillation; distilling large models to small ones

6.2 Inference Optimization

• Inference Frameworks: Selection and performance comparison of TensorRT, ONNX Runtime, and vLLM
• KV Cache: KV caching mechanism for autoregressive generation; PagedAttention memory management
• Batching Strategies: Continuous Batching, Dynamic Batching for throughput improvement
• Speculative Decoding: Using small models to predict large model outputs; accelerating autoregressive generation

6.3 Distributed Training

• Parallelism Strategies: Principles and applicable scenarios for data parallelism (DDP), model parallelism (tensor/pipeline parallelism)
• ZeRO Optimization: ZeRO-1/2/3 optimize optimizer states/gradients/parameters memory usage respectively
• Mixed Precision Training: FP16/BF16 forward + FP32 master weights; Loss Scaling prevents gradient underflow
• Communication Optimization: Gradient accumulation, communication-computation overlap, Ring AllReduce

6.4 Engineering Answer Strategy

1. Problem-driven: First state what problem you encountered (high latency/insufficient memory/low throughput)
2. Solution comparison: List 2-3 approaches, explain selection rationale
3. Quantified results: Provide specific numbers before and after optimization (latency reduced by X%, throughput increased by Yx)
4. Lessons learned: Share actual problems and solutions from deployment

7. Business Scenarios and Project Experience: The Decisive Round of AI Interviews

Technical ability is just the entry ticket. Business understanding and project delivery capability are what determine the offer. Interviewers use deep project dives to assess your comprehensive abilities.

7.1 Project Presentation Framework (Enhanced STAR Method)

1. Business Context: What business problem does the project solve? How large is the impact?
2. Technical Solution: Why this algorithm/model? What was compared against the baseline?
3. Challenges and Innovation: What was the biggest challenge? What innovations did you make?
4. Results and Impact: How much did core metrics improve? How was business impact quantified?
5. Retrospective: What would you improve if you did it again?

7.2 Common Business Scenario Topics

• Recommendation Systems: Funnel architecture of recall (dual-tower/ANN) → coarse ranking → fine ranking → re-ranking; cold start strategies
• Search Ranking: Query understanding, semantic matching, LTR model selection
• Risk Control and Anti-Fraud: Class imbalance handling, feature timeliness, real-time requirements
• Intelligent Customer Service: Intent recognition, multi-turn dialogue management, knowledge base construction
• Content Safety: Multi-modal moderation, balancing false positive rate and recall

7.3 Project Experience Answer Strategy

1. Start with business value: Help the interviewer understand the project's importance
2. Combine technical depth with business: Don't show off; explain why the technical solution fits the business scenario
3. Data-driven decisions: Use A/B test results and online metric changes to support your solution choices
4. Be honest about shortcomings: Proactively mention project regrets and improvement directions; this is more impressive than avoiding issues

AI Interview Preparation Tips

Facing the vast knowledge system of 7 modules, preparation strategy matters more than blindly practicing questions.

• Identify gaps by module: Self-assess first, then focus on weak modules
• Emphasize derivation and handwriting: Interviews often require whiteboard derivations; understanding ≠ ability to write
• Deep-dive into project experience: Prepare 3 levels of depth for each project's follow-up questions
• Stay current with frontiers: Read 1-2 latest papers per week; maintain technical sensitivity
• Practice mock interviews: Find peers or seniors for mock interviews; train expression logic

Beyond interview prep, don't forget to prepare a professional resume to showcase your project experience and technical skills. We recommend using a resume generator that offers multiple tech-position-style templates, smart formatting to highlight project highlights, and one-click PDF export. Strong technical skills deserve an equally impressive resume to land that AI algorithm position offer.

FAQ

Q1: How many rounds are typical for AI algorithm interviews? What's the focus of each round?

Typically 3-4 rounds: Round 1 focuses on fundamentals (math + ML + DL), Round 2 on deep project dives, Round 3 on system design and engineering, and the HR round on soft skills and career planning. Some companies also have a written test covering coding and math basics.

Q2: What if I don't have large model project experience?

Quickly start a fine-tuning project (e.g., use LoRA to fine-tune Llama), deploy it to Hugging Face Spaces, and write a detailed technical blog post. Demonstrating learning ability and hands-on skills in the interview is far better than having no experience at all.

Q3: What if I can't remember math derivations?

Don't memorize blindly. Understand the logical chain of derivations, remember key steps and core ideas, and derive step by step from first principles during interviews. Interviewers care more about whether your derivation logic is clear than whether the result is perfectly correct.

Q4: Do I still need to prepare traditional ML in depth?

Yes. While large models are the hot topic, traditional ML tests your modeling thinking and theoretical foundation, which is an important basis for interviewers to judge whether you truly understand algorithms. SVM derivation, GBDT principles, and bias-variance tradeoff remain high-frequency topics.

Q5: How should I prepare for engineering-related questions?

If you lack actual deployment experience, try deploying a model service with Docker, doing inference optimization with vLLM or TensorRT, and recording performance comparisons before and after optimization. Being able to cite specific numbers and lessons learned during interviews is far more convincing than purely theoretical answers.

Q6: How should I write project experience in my resume for maximum impact?

Use the format of one sentence covering business value + technical solution + quantified results for each project. For example: "Designed BERT-based text classification system, F1 improved 12%, online QPS reached 5000." We recommend using a resume generator with smart formatting to make project highlights stand out at a glance.