These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparation for Meta is distinct from other tech giants. The process is highly standardized, rigorous, and prioritizes speed and communication. You should approach your preparation by focusing on four core pillars of evaluation.

Coding and Algorithmic Fluency – You must be able to translate thoughts into bug-free code rapidly. Meta places a premium on solving two distinct problems within a single 45-minute session. Evaluators look for optimal time and space complexity, clean syntax, and the ability to verify your own code without running it.

System and ML Design – For AI Engineers, this is often the deciding factor. You will be evaluated on your ability to design scalable systems (e.g., "Design a recommendation feed") or specific AI workflows (e.g., "Build a weapon detection filter for Marketplace"). You need to demonstrate how you handle data ingestion, model selection, training pipelines, and inference at scale.

Domain Proficiency (AI/HPC) – Depending on the specific role (e.g., AI Systems Performance vs. Agentic AI), you will be tested on your depth in the stack. This could range from understanding RDMA and GPU interconnects to designing reinforcement learning loops for automated testing. You must show you understand what happens "under the hood" of libraries like PyTorch.

Meta Values and Behavioral Signals – Meta evaluates candidates on their ability to "Move Fast" and "Focus on Impact." You will face behavioral questions designed to test how you handle conflict, drive consensus in cross-functional teams, and manage ambiguity. Your answers should be structured and highlight your personal contribution.

The interview process for the AI Engineer role is designed to be efficient but intense. It typically begins with a recruiter screening to assess your background and alignment with open roles. This is followed by a technical phone screen (or video screen) which is almost exclusively focused on coding algorithms. You must pass this hurdle to proceed to the onsite loop.

The "onsite" (currently virtual) is a comprehensive loop consisting of 4 to 5 separate interviews. You can expect two rounds of coding, one or two rounds of system design (either general distributed systems or ML-specific design), and a behavioral round often referred to as the "Jedi" round. For senior or specialized roles, an additional deep-dive interview into your specific domain expertise (such as HPC networking or Agentic AI) may be included.

Meta's philosophy is data-driven. Interviewers submit detailed feedback independently, and a hiring committee reviews the packet holistically. They look for "strong signals" in specific areas. A "neutral" performance is often insufficient; you need to clearly demonstrate strength in coding and design. The process can move quickly, but delays in feedback (as noted in some recent candidate experiences) can occasionally occur depending on hiring committee schedules.

This timeline illustrates the typical progression from your first contact to a potential offer. Note that the Technical Screen is a strict gatekeeper; strict time management here is critical. The Onsite Loop is an endurance test—ensure you manage your energy levels to maintain peak performance through the final behavioral round.

To succeed, you must master the specific areas Meta evaluates. Based on recent interview data, here is what you need to prepare for.

Coding & Algorithms

This is the baseline requirement. Meta interviews often require solving two medium-to-hard problems in roughly 35–40 minutes of coding time.

Be ready to go over:

• Trees and Graphs: Tree traversals, lowest common ancestor, and graph search algorithms (BFS/DFS) are extremely common.
• Arrays and Strings: Sliding window, two pointers, and manipulation of large datasets.
• Recursion and Dynamic Programming: You must be able to identify overlapping subproblems and optimize them.
• Data Structures: Know when to use a Heap vs. a HashMap vs. a Trie.

Example questions or scenarios:

• "Reverse a binary tree or specific levels of a tree."
• "Find the k-th largest element in a stream of data."
• "Serialize and deserialize a binary tree."

Machine Learning System Design

This round tests your ability to take an ambiguous problem and define a workable, scalable AI solution. You are expected to drive the conversation.

Be ready to go over:

• Data Engineering: How do you collect, clean, and label data at scale? How do you handle class imbalance?
• Model Lifecycle: Feature engineering, model selection (why deep learning vs. tree-based?), training pipelines, and evaluation metrics.
• Productionization: Inference latency, caching, online vs. offline learning, and safety filtering.
• Advanced concepts: Multi-modal learning, active learning, and specifically for Meta, content moderation systems.

Example questions or scenarios:

• "Design a filter for Facebook Marketplace that prevents users from listing prohibited items (e.g., weapons)."
• "Design the recommendation system for Instagram Reels."
• "How would you build a system to detect hate speech in multiple languages?"

AI Infrastructure & Performance

For roles focused on HPC (High-Performance Computing) or AI Systems, this area is critical. You will be evaluated on your understanding of the hardware and software stack that enables AI.

Be ready to go over:

• Distributed Training: Data parallelism, model parallelism, and pipeline parallelism.
• Networking: Understanding RDMA, InfiniBand, RoCE, and congestion control mechanisms.
• Communication Libraries: How NCCL or MPI work for inter-GPU communication.
• Performance Debugging: Identifying bottlenecks in the stack (compute-bound vs. memory-bound vs. network-bound).

Example questions or scenarios:

• "How do you troubleshoot a distributed training job that is hanging or slow?"
• "Explain the trade-offs between different collective communication primitives (AllReduce vs. AllGather)."
• "How would you optimize a PyTorch model for lower latency on specific hardware?"

As an AI Engineer at Meta, your day-to-day work is characterized by high autonomy and high impact. You are responsible for the end-to-end lifecycle of AI systems, from architectural design to production deployment.

For engineers in the AI Infrastructure track, you will build and evolve the network fabrics that connect thousands of GPUs. You will work on ensuring these networks run smoothly under the strain of massive RDMA workloads. You will constantly profile the stack—analyzing host networking, communication libraries, and scheduling infrastructure—to squeeze out every bit of performance.

For engineers in product-facing or Reality Labs roles, you will design agentic workflows and intelligent frameworks. This involves creating autonomous systems that can assess product quality, identify edge cases, and enhance reliability. You will collaborate closely with research scientists to integrate the latest ML advances into practical tools, bridging the gap between a research paper and a reliable feature used by millions.