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Coding & Data Structures ----------- ML Foundations & Theory
### System ML & Architecture Design
This area tests your ability to take high-level product requirements and design scalable, reliable machine learning systems. Interviewers look for structured thinking, database selection, candidate generation strategies, real-time inference constraints, and privacy-preserving architectural choices.
**Be ready to go over:**
- **Recommendation & Ranking Systems** – Candidate generation, two-stage retrieval networks, multi-task learning for ranking, and balancing diversity versus click-through rate (**CTR**).
- **On-Device Audio & Signal Processing** – Audio feature extraction (spectrograms, MFCCs), streaming temporal models, real-time classification on low-power neural engines, and noise mitigation.
- **Language Models & RAG Architectures** – Comparative design between parameter-efficient fine-tuning (**LoRA**, **P-Tuning**) and **Retrieval-Augmented Generation**, including vector indexing and latency bounds.
- **Advanced concepts (less common)** – Multi-modal fusion networks, on-device federated learning architectures, and edge-optimized hardware acceleration strategies.
**Example scenarios:**
- "Design a healthy food recommendation system, including data pipelines, feature engineering, loss function selection, and continuous online evaluation."
- "Architect an audio analysis system to detect neurological disorders like Parkinson's disease from continuous spoken audio stream data."
- "Design a personalized video panel on a homepage that recommends content strictly based on semantic similarity while preventing filter bubbles."
### ML Foundations & Algorithmic Implementation
This area tests whether you understand machine learning beyond calling pre-built library functions. You will be expected to code fundamental algorithms from scratch, explain loss landscape geometry, and analyze model dynamics theoretically.
**Be ready to go over:**
- **Core Unsupervised & Supervised Algorithms** – Implementing **K-Means**, expectation-maximization, decision trees, and gradient descent variants using standard mathematical operations.
- **Transformer Mechanics & Fine-Tuning** – Writing self-attention modules, multi-head attention projections, dynamic positional encodings, and parameter-efficient adaptations like **LoRA**.
- **Interpretability & Scientific Inference** – Explaining causal tracing, activation patching, saliency maps, and applying **Bayesian statistical methods** for model evaluation.
- **Advanced concepts (less common)** – Neural architecture search, activation quantization schemes (e.g., INT4/INT8 precision math), and direct preference optimization (**DPO**).
**Example scenarios:**
- "Write a functional implementation of the K-Means clustering algorithm using standard matrix algebra routines without external ML libraries."
- "Derive and code a Low-Rank Adaptation (LoRA) layer for a standard transformer linear projection step."
- "How would you design an experimental framework using activation patching to trace factual recall in a 7-billion parameter language model?"
### Coding, Systems & Execution
**Apple** research scientists must write clean, production-ready code. This evaluation area focuses on classical data structures, custom state management, time/space complexity analysis, and object-oriented design in **Python** or **C++**.
**Be ready to go over:**
- **Custom Data Structure Design** – Building specialized memory caches, double-ended lookup mechanisms, and stateful tracking systems with strict asymptotic space and time complexities.
- **Stateful Expiration & Systems Logic** – Managing **Time-To-Live (TTL)** expiration policies, queue eviction strategies, and precise timestamp evaluations.
- **Tree & Graph Traversal** – Implementing recursive and iterative depth-first (**DFS**) and breadth-first (**BFS**) algorithms for non-binary $N$-ary trees.
- **Advanced concepts (less common)** – Custom garbage collection logic, thread synchronization primitives, and memory alignment considerations for high-performance computing.
**Example scenarios:**
- "Implement a thread-safe cache system supporting key-value storage with per-item TTL expiration, ensuring stale keys are lazily and proactively purged."
- "Write an efficient algorithm to navigate an $N$-ary decision tree and return path costs across arbitrary branch nodes."
- "Design an array-balancing algorithm that minimizes computational overhead while running within tight memory constraints."
