Meta AI Engineer Interview Guide

Meta has been making headlines for offering elite AI researchers packages reportedly in the $100M+ range. Those numbers are real, but they describe a tiny sliver of marquee hires, not the typical offer. From hundreds of mock interviews we've run for Meta AI loops, the pattern that sinks candidates isn't weak research chops. It's underestimating how much this role demands you bridge research and production in the same breath.

Meta AI Engineer Role

The role is formally titled "Research Scientist," but don't let that fool you into thinking it's a pure lab position. You'll design novel ML architectures and run large-scale experiments on internal GPU clusters, then collaborate directly with engineering and product teams to translate those findings into real user-facing impact. Meta's own job descriptions emphasize both first-authored publications at venues like NeurIPS and ICML and a clear path from research to product integration.

A Typical Week

What surprises most candidates is how much ownership this role carries beyond the research itself. Meta expects you to collaborate with product managers, infra engineers, and data teams to push your work toward production, rather than tossing a paper over the wall to a separate integration team. If you're coming from a pure academic lab, that cross-functional calendar load is the single biggest adjustment.

Projects & Impact Areas

Recommendation systems and generative AI form the two heaviest pillars of Meta's AI research investment right now. The Llama model family and the Meta AI assistant drive work on training, alignment, and agentic systems, while the newly announced Meta Superintelligence Lab (MSL) signals a push toward frontier model capabilities. Reality Labs adds a third, distinct flavor: on-device ML for products like smart glasses, where model efficiency and edge deployment constraints matter more than raw parameter count.

Skills & What's Expected

Expert-level ML, math, and statistics are the price of admission, but the underrated skill is research communication. The role descriptions emphasize publishing, open-sourcing reproducible work, and collaborating across teams, which means being a brilliant but silent researcher limits your ceiling. Infrastructure and cloud deployment knowledge sits at a medium bar (distributed training and efficient inference matter, but you're not expected to be a systems architect), and Python is the primary language listed on every req.

Levels & Career Growth

The widget shows the level bands, so here's the context it can't. E7 and E8 expectations center on setting multi-year research agendas and influencing entire organizations, which is a qualitatively different job than the project-scoped execution at E5. The single biggest promotion blocker from E5 to E6, per Meta's own ladder language? Demonstrating impact beyond your immediate team by leading ambiguous, cross-team research initiatives. The IC track extends to E8 (Principal) and beyond without ever requiring you to manage people.

Work Culture

Meta lists this role as hybrid, with the primary location in Menlo Park. The engineering culture prizes speed and code ownership: you're evaluated on whether your research moves toward impact, not just whether it's scientifically novel. That dual expectation can feel like serving two masters, but it also means your work reaches users faster than at organizations with a harder wall between research and product.

Meta AI Engineer Compensation

Meta's RSUs vest over four years, and annual refresh grants tied to performance ratings can meaningfully shift your trajectory. The refresh mechanism matters more than most candidates realize: the source data confirms these are common and performance-based, which means your comp two years in depends heavily on how your work is rated, not just what you negotiated at signing. For AI roles specifically, Meta has structured packages for top researchers that can reach hundreds of millions over four years, with significant first-year compensation, a signal that the company is willing to front-load value to win talent in the current AI hiring war.

Base salary, sign-on bonus, and RSU grants are all negotiable, but the source data and candidate reports suggest RSUs are the most flexible component. Push hardest on the initial equity grant and sign-on bonus rather than base, where bands tend to be tighter. One Meta-specific angle worth exploiting: the company's very public arms race for AI talent (Llama team expansions, the new Meta Superintelligence Lab) gives you real leverage even without a competing offer, because recruiters know how aggressively Google DeepMind and OpenAI are bidding on the same candidates.

Meta AI Engineer Interview Process

The typical timeline from first recruiter call to signed offer stretches to around 24 weeks, from what candidates report. That's long even by big-tech standards. Two back-to-back coding rounds on the onsite are the biggest filter, and a supplementary third coding session gets added if the hiring committee sees mixed signals in your packet.

Your interviewers don't decide whether you get hired. Written feedback from each session goes to an independent hiring committee that never met you, which means your ability to communicate clearly in the room matters even more than usual, because the committee is reading a secondhand account of your thought process. If that supplementary coding round appears on your schedule, treat it as a tiebreaker where clean explanation counts as much as a correct solution.

Meta AI Engineer Interview Questions

from collections import deque
from typing import List


def shortest_window_at_least_k(deltas: List[int], k: int) -> int:
    """Return length of shortest contiguous subarray with sum >= k, or -1.

    Works with negative numbers using prefix sums and a monotonic deque.
    Time: O(n), Space: O(n).
    """
    n = len(deltas)

    # Prefix sums: P[0] = 0, P[i] = sum(deltas[:i])
    prefix = [0] * (n + 1)
    for i, x in enumerate(deltas, start=1):
        prefix[i] = prefix[i - 1] + x

    # Deque of indices of prefix sums, increasing by prefix value.
    dq = deque()
    best = n + 1

    for i in range(n + 1):
        # If current prefix minus smallest prefix in deque reaches k,
        # update answer and remove that index since any later i will only be longer.
        while dq and prefix[i] - prefix[dq[0]] >= k:
            best = min(best, i - dq[0])
            dq.popleft()

        # Maintain increasing prefix sums in deque.
        # If prefix[i] <= prefix[last], last is dominated (worse start) and can be removed.
        while dq and prefix[i] <= prefix[dq[-1]]:
            dq.pop()

        dq.append(i)

    return -1 if best == n + 1 else best

The distribution's real story isn't any single area; it's that ML theory, deep learning, and LLM/agents questions collectively dwarf coding, yet most candidates over-index on algorithm prep alone. When a Llama-focused system design question requires you to reason about DPO vs. PPO tradeoffs and explain optimizer instability in a 7B parameter training run, those areas stop being separate buckets and start compounding on each other. Skipping any slice of the distribution, even a thin one like math/probability, is risky because Meta's loop is long enough that a single weak signal has nowhere to hide across five technical rounds.

Drill all seven areas with worked solutions at datainterview.com/questions.

How to Prepare for Meta AI Engineer Interviews

Meta's north star right now is AI, and the company's actions back that up. The PyTorch-native agentic stack gives you a window into where the Llama ecosystem is heading: not just open-weight models, but a full developer platform for building AI agents. Meanwhile, Reality Labs is separating its Quest VR platform from Worlds and shifting Worlds almost exclusively to mobile, which means on-device ML efficiency is becoming a first-class engineering problem there.

The 2026 roadmap from Zuckerberg doubles down on AI-driven ad performance and frontier model research. If you're interviewing soon, your "why Meta" answer needs to be sharper than "I want to work on AI at scale." Name the specific surface you'd improve, whether that's Llama's RLHF pipeline, recommendation ranking for Reels, or multimodal models for AR glasses. Tie your experience to a product bet they've already made public.

Vague answers about impact or mission won't separate you from the other fifty candidates that week. Reference a real blog post, a specific Llama release, or a concrete Reality Labs constraint like running inference on battery-powered hardware.

Try a Real Interview Question

from typing import List


def top_k_frequent_tokens(tokens: List[str], k: int) -> List[str]:
    """Return the k most frequent tokens, sorting by (-count, token)."""
    pass

from typing import List
import heapq
from collections import Counter


def top_k_frequent_tokens(tokens: List[str], k: int) -> List[str]:
    """Return the k most frequent tokens, sorting by (-count, token).

    If k <= 0, returns an empty list.
    """
    if k <= 0:
        return []

    counts = Counter(tokens)

    heap = [(-cnt, tok) for tok, cnt in counts.items()]
    heapq.heapify(heap)

    out: List[str] = []
    for _ in range(min(k, len(heap))):
        _, tok = heapq.heappop(heap)
        out.append(tok)
    return out

Meta's coding rounds favor problems where the brute-force solution is obvious but the optimal one requires you to spot a graph or dynamic programming structure hiding underneath. What makes this loop distinct is that Meta runs batch-day onsites where multiple algorithm sessions happen in sequence, so consistency across hours of problem-solving matters as much as peak performance on any single question.

Practice in timed sets rather than one-offs. datainterview.com/coding lets you simulate that kind of sustained pressure, which is where most people's code quality quietly falls apart.

Test Your Readiness

If any of those questions exposed a gap, especially in math or the newer GenAI material, close it before your loop at datainterview.com/questions.