Google AI Engineer Interview Guide

Most candidates prep for this role like it's a software engineering loop with some ML sprinkled in. From hundreds of mock interviews we've run, the people who struggle aren't weak engineers. They're strong engineers who didn't realize Google's AI Researcher interviews demand whiteboard-level math derivations and production-grade JAX code in the same sitting.

Google AI Engineer Role

Google's AI Researcher role sits between publishing novel research and shipping models into products like Search, Gemini, and Vertex AI. You might prototype a new model architecture one month, then spend the next hardening it for serving at scale on TPU infrastructure. Success after year one means a meaningful contribution to a launched model or a top-tier publication, and the strongest performers deliver both.

A Typical Week

The split that surprises most people is how much time goes to cross-team coordination. You're syncing with adjacent research groups, participating in paper reading sessions, and sitting in design reviews, not just running experiments solo. Pure heads-down research time is real but competes with the collaboration overhead that comes from working inside a monorepo shared across thousands of engineers.

Projects & Impact Areas

Gemini model work feeds directly into Search and Google's broader product suite, while Vertex AI features you build ship to Cloud customers with very different latency and reliability requirements. On-device ML for Pixel, meanwhile, forces you into memory and compute constraints that feel nothing like training on TPU pods. These project areas pull on different skills, and your team placement after hiring determines which tradeoffs dominate your day-to-day.

Skills & What's Expected

The underrated skill is raw mathematics. Expert-level fluency in optimization theory, probability, and linear algebra isn't a nice-to-have; interviewers will ask you to derive loss function gradients and reason about regularization properties on the spot. Software engineering expectations run higher than at most research labs, too, because Google's culture demands readable, tested code even for research prototypes. If your code works but reads like a notebook dump, that's a real problem in this environment.

Levels & Career Growth

Most external hires land at L4 or L5. The jump between them hinges on whether you can independently own end-to-end model development, including problem selection, rather than executing tasks scoped by someone senior. The biggest promotion blocker at L5, from what candidates report, is demonstrating influence beyond your own project: showing that your technical direction shaped what adjacent teams built.

Work Culture

The role is hybrid, with flexible arrangements that vary by team and location. The pace feels intense but structured, and behavioral interviews explicitly assess collaborative, low-ego, data-driven behavior. Being technically brilliant but dismissive of a teammate's perspective will hurt your hiring packet more than a missed coding question.

Google AI Engineer Compensation

The vesting schedule above deserves a closer look. Because the grant is front-loaded, your year-one and year-two payouts will be noticeably larger than years three and four. From what candidates report, refresh grants can help smooth that curve, but they're awarded based on your performance review cycle and vary widely. Plan your finances around the possibility that total comp dips in the back half of your initial grant rather than assuming refreshers will perfectly fill the gap.

When negotiating, RSU grants tend to be the component with the most room to move. A written competing offer from another company in the AI space is, from what candidates consistently report, the strongest catalyst for a recruiter to revisit the equity number. If you hold a PhD or have a strong publication record in venues like NeurIPS or ICML, that background can strengthen your case for a larger initial grant or a sign-on bonus, since Google's Research Scientist ladder explicitly values research output at every level.

Google AI Engineer Interview Process

From what candidates report, the post-onsite phase is where Google's process feels most alien. Your interviewers submit structured written feedback to a hiring committee they're not part of, and that committee debates your packet without ever having met you. This means your performance is filtered through someone else's notes. If you solved a Gemini-scale system design question brilliantly but didn't vocalize your reasoning around TPU serving tradeoffs or evaluation metric choices, the written feedback may not reflect what you actually know.

The non-obvious implication: you're optimizing for two audiences simultaneously. You need to impress the person in the room, yes, but you also need to make their job as a writer easy. Candidates who've interviewed at places like Meta or Amazon, where the interviewer holds direct voting power, often underestimate how much Google's committee-based structure rewards explicit, narrated reasoning over quiet problem-solving. Spell out why you chose one attention mechanism over another, or why you'd pick a specific distillation approach for on-device Pixel inference. That specificity gives your interviewer concrete material to quote, which is ultimately what the committee weighs.

Google AI Engineer Interview Questions

import torch
import torch.nn.functional as F


def masked_softmax_xent(logits: torch.Tensor, targets: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
    """Numerically stable masked softmax cross-entropy.

    Args:
        logits: Float tensor of shape [B, T, V].
        targets: Long tensor of shape [B, T] with class indices in [0, V).
        mask: Float/bool tensor of shape [B, T], 1 for valid tokens, 0 for padding.

    Returns:
        Scalar tensor, mean loss over valid tokens.
    """
    if logits.ndim != 3:
        raise ValueError(f"logits must be [B,T,V], got {logits.shape}")
    if targets.shape != logits.shape[:2]:
        raise ValueError(f"targets must be [B,T], got {targets.shape}")
    if mask.shape != logits.shape[:2]:
        raise ValueError(f"mask must be [B,T], got {mask.shape}")

    # Ensure types
    targets = targets.long()
    mask = mask.to(dtype=logits.dtype)

    # log_softmax is already stable (internally uses log-sum-exp trick)
    log_probs = F.log_softmax(logits, dim=-1)  # [B,T,V]

    # Gather log p(target)
    # targets.unsqueeze(-1): [B,T,1]
    nll = -torch.gather(log_probs, dim=-1, index=targets.unsqueeze(-1)).squeeze(-1)  # [B,T]

    # Apply mask and normalize by number of valid tokens
    nll = nll * mask
    denom = mask.sum().clamp_min(1.0)  # avoid divide-by-zero on empty batches
    return nll.sum() / denom


if __name__ == "__main__":
    # Quick sanity check
    B, T, V = 2, 3, 5
    torch.manual_seed(0)
    logits = torch.randn(B, T, V)
    targets = torch.randint(0, V, (B, T))
    mask = torch.tensor([[1, 1, 0], [1, 0, 0]], dtype=torch.float32)
    loss = masked_softmax_xent(logits, targets, mask)
    print(loss.item())

import torch
import torch.nn.functional as F


def info_nce_symmetric(q: torch.Tensor, k: torch.Tensor, tau: float = 0.07) -> torch.Tensor:
    """Symmetric InfoNCE with in-batch negatives.

    Args:
        q: Float tensor [B, D], query embeddings.
        k: Float tensor [B, D], key embeddings (positive pairs aligned by index).
        tau: Temperature scalar.

    Returns:
        Scalar tensor, mean symmetric InfoNCE loss.
    """
    if q.ndim != 2 or k.ndim != 2:
        raise ValueError(f"q and k must be [B,D], got {q.shape}, {k.shape}")
    if q.shape != k.shape:
        raise ValueError(f"q and k must have same shape, got {q.shape} vs {k.shape}")
    if tau <= 0:
        raise ValueError("tau must be > 0")

    # Normalize to unit vectors to make dot product equal cosine similarity
    q = F.normalize(q, p=2, dim=-1)
    k = F.normalize(k, p=2, dim=-1)

    # Similarity matrix [B,B] without constructing [B,B,D]
    logits = (q @ k.t()) / tau

    # Correct match for row i is column i
    B = q.shape[0]
    labels = torch.arange(B, device=q.device)

    loss_q_to_k = F.cross_entropy(logits, labels)
    loss_k_to_q = F.cross_entropy(logits.t(), labels)

    return 0.5 * (loss_q_to_k + loss_k_to_q)


if __name__ == "__main__":
    torch.manual_seed(0)
    B, D = 8, 128
    q = torch.randn(B, D)
    k = torch.randn(B, D)
    print(info_nce_symmetric(q, k, tau=0.1).item())

What jumps out isn't any single category but how the math/stats and ML theory slices compound with everything else. A question about mode collapse in a Google Photos VAE doesn't stay conceptual for long; your interviewer will push you to derive the KL term's behavior, sketch the gradient, and propose a fix that accounts for decoder capacity. Skipping the foundational math prep because it looks like a smaller slice is the most common miscalculation candidates report, since those derivation skills get tested inside the deep learning and GenAI rounds too. From what candidates describe, the interview rewards depth over breadth: you're better off being able to implement a masked softmax cross-entropy loss from scratch in PyTorch and explain every numerical stability choice than having surface-level familiarity with ten architectures.

Build reps across all the question areas at datainterview.com/questions.

How to Prepare for Google AI Engineer Interviews

Google's strategic bets right now cluster around autonomous AI agents, evolving the Gemini model family, and transforming Search from a retrieval system into a synthesized answer engine. With Search & Other generating 56.98% of Alphabet's fiscal year 2025 revenue, that segment's gravity pulls AI Engineers into problems like query understanding, ranking, and grounding model outputs in real-time information.

Your "why Google" answer should name a specific product surface and a real constraint you find interesting. Saying you want to build Gemini's agentic tool-use capabilities for Vertex AI customers, or that you're drawn to the latency constraints of on-device inference on Pixel, tells an interviewer you've done homework. Vague enthusiasm about "working on AI at scale" won't differentiate you from the hundreds of other candidates in the pipeline. Pull a concrete detail from Google I/O or a recent Alphabet earnings call and connect it to something you've actually built or studied.

Try a Real Interview Question

from typing import List


def top_k_indices(scores: List[float], k: int) -> List[int]:
    """Return indices of the top k scores sorted by score descending, then index ascending.

    Args:
        scores: List of floats of length N.
        k: Number of indices to return.

    Returns:
        A list of indices following the required ordering.
    """
    pass

from typing import List
import heapq
import math


def top_k_indices(scores: List[float], k: int) -> List[int]:
    """Return indices of the top k scores sorted by score descending, then index ascending.

    Uses a size-k min-heap to avoid sorting all N elements when k << N.
    Tie-breaking is stable by preferring smaller indices when scores are equal.

    Args:
        scores: List of floats of length N.
        k: Number of indices to return.

    Returns:
        A list of indices following the required ordering.

    Raises:
        ValueError: If k is negative or if scores contain NaN.
    """
    if k < 0:
        raise ValueError("k must be non-negative")

    n = len(scores)
    if n == 0 or k == 0:
        return []

    for s in scores:
        if isinstance(s, float) and math.isnan(s):
            raise ValueError("scores must not contain NaN")

    if k >= n:
        return sorted(range(n), key=lambda i: (-scores[i], i))

    heap: List[tuple[float, int]] = []

    for i, s in enumerate(scores):
        item = (s, -i)
        if len(heap) < k:
            heapq.heappush(heap, item)
        else:
            if item > heap[0]:
                heapq.heapreplace(heap, item)

    top = [(-neg_i, s) for (s, neg_i) in heap]
    top.sort(key=lambda pair: (-pair[1], pair[0]))
    return [idx for idx, _ in top]

Google's ML coding rounds require you to produce working code in PyTorch, JAX, or TensorFlow, so candidates who've only practiced algorithmic problems (trees, graphs, sorting) often hit a wall when asked to implement a training loop or a custom layer from scratch. Building that muscle memory before your onsite matters more than cramming theory the night before. Drill ML-specific coding problems regularly at datainterview.com/coding.

Test Your Readiness

Use datainterview.com/questions to practice across every question category you'll face in Google's AI Engineer loop, from deep learning fundamentals to GenAI and system design.