- Agentic Workflows & State Loops – Architecting autonomous systems that plan, execute tool calls, inspect outputs, and iteratively self-correct.
- RAG Pipeline Architecture – Chunking strategies, hybrid search (combining dense vector retrieval with sparse keyword matching), re-ranking algorithms, and context window optimization.
- Context Engineering & Prompt Management – System prompt optimization, managing structured outputs (e.g., JSON schemas), and context compression techniques.
- Advanced concepts (less common) – Graph-based agent workflows (LangGraph paradigms), dynamic tool registration, and guardrail integration for real-time safety enforcement.
Example questions or scenarios:
- "Architect an automated code-fix agent that receives build logs, isolates failing test cases using an investigate agent, and generates targeted code edits using a resolve agent."
- "How do you handle retrieval failures or hallucination drift within an enterprise multi-tenant RAG pipeline?"
ML System Design & Large-Scale Recommendation
This pillar evaluates your end-to-end design capabilities for massive-scale recommendation, ranking, and machine learning platforms.
In these rounds, you must bridge high-level system components (load balancers, message queues, feature stores) with low-level ML details (candidate generation, vector index sharding, model distillation, and online inference serving). You should define clear metrics for success, such as Click-Through Rate (CTR), engagement depth, latency budgets, and system availability.
Be ready to go over:
- Multi-Stage Recommendation Pipelines – Candidate generation (retrieval), scoring/ranking (heavy neural networks), and final re-ranking/diversity filtering.
- High-Throughput LLM Serving Systems – Continuous batching, KV cache optimization (PagedAttention), model parallelism (Tensor vs. Pipeline parallelism), and speculative decoding.
- Real-time Feature Engineering – Streaming data pipelines, low-latency key-value stores for user feature lookup, and feature store alignment between training and serving.
- Advanced concepts (less common) – Multi-task learning architectures (e.g., MMoE), offline-to-online metric correlation, and quantization strategies (INT8/FP8) for multi-GPU serving.
Example questions or scenarios:
- "Design the end-to-end recommendation and ranking system for Meta Marketplace items."
- "Architect an LLM inference service capable of serving Llama models to millions of concurrent users with sub-100ms latency constraints."
AI-Enabled Coding & Algorithmic Problem Solving
Meta's coding rounds assess your fluency with data structures, algorithm optimization, and your ability to navigate larger context codebases—frequently incorporating AI-assisted execution environments.
Interviewers evaluate how quickly you turn abstract logic into bug-free code. Problems often involve graph traversals, dynamic programming, matrix navigation, interval merging, and resource scheduling algorithms.
Be ready to go over:
- Matrix & Graph Search – Breadth-First Search (BFS), Depth-First Search (DFS), and A* algorithms for maze exploration and interactive state-space navigation.
- Greedy Algorithms & Intervals – Interval overlap detection, sorting strategies, and allocation algorithms (e.g., minimum arrows to burst balloons).
- Optimization & Scheduling – Dynamic programming and constraint satisfaction algorithms for task scheduling under complex worker/resource constraints.
- Advanced concepts (less common) – Interactive state-machine navigation via restricted helper APIs, custom memory management, and C/C++ host memory optimization.
Example questions or scenarios:
- "Given a grid matrix with custom exposed API methods, write an interactive search algorithm for an agent navigating to a target destination."
- "Implement an efficient solution to solve the card game 3-sum problem targeting specific combination thresholds."
Model Evaluation, Embeddings & Vector Search
This area tests your understanding of statistical validation, model quality assessment, and vector representation infrastructure.
You will need to demonstrate how to rigorously evaluate machine learning models beyond simple accuracy metrics, accounting for latency, resource usage, and generation safety. Additionally, you should understand how high-dimensional vectors are indexed and queried at scale.
Be ready to go over:
- LLM Evaluation Frameworks – Designing robust evaluation datasets, automated evaluation pipelines (LLM-as-a-Judge), unit testing for model generations, and human-in-the-loop validation.
- Vector Search & Embeddings – Nearest Neighbor search algorithms, HNSW indexing, Product Quantization (PQ), and dimensionality reduction techniques.
- Model Fine-Tuning & Adaptation – LoRA, QLoRA, and supervised fine-tuning protocols using PyTorch across multi-node GPU clusters.
- Advanced concepts (less common) – Direct Preference Optimization (DPO), RLHF reward model alignment, and semantic caching architectures.
Example questions or scenarios:
- "How would you design a continuous evaluation pipeline to measure drift and safety compliance in an active customer support LLM?"
- "Explain how to scale vector search indexing across a distributed cluster when serving billions of item embeddings."
