Google DeepMind AI Engineer Interview Guide

DeepMind runs its own hiring committee separate from Google's standard process. You can ace every single interview round and still get rejected if your packet doesn't show research-grade ML depth alongside production engineering skill. The candidates who struggle most, from what we've seen coaching for this role, aren't weak on algorithms or ML theory. They're strong at one and shaky at the other.

Google DeepMind AI Engineer Role

Your job is turning Gemini model checkpoints into things that actually work in production. A concrete example from the day-in-life data: you might spend Tuesday prototyping a chain-of-thought steering system for an agentic task planner, writing eval assertions to score tool-call sequences against gold trajectories, then on Thursday presenting that prototype live to your engineering pod and fielding questions about latency and cost tradeoffs. Success after year one means you've taken a research prototype through safety review and into a deployed system, owning the eval harness, the infrastructure config, and the cross-team coordination that made it ship.

A Typical Week

What the schedule doesn't convey is how quiet the prototyping blocks actually are. At a company with 180,000+ employees, getting six consecutive hours of uninterrupted coding time on a Tuesday feels almost suspicious. The other thing worth flagging: the writing load (design docs, eval summaries, "Alternatives Considered" sections) isn't busywork. Those artifacts form the packet that your promotion committee reads, so treating them as an afterthought is a career mistake.

Projects & Impact Areas

Gemini training, fine-tuning, and RLHF pipelines anchor the work, but the day-in-life data reveals how much time goes toward eval infrastructure and agentic AI prototyping. You're building systems where an agent selects tools across multi-step workflows, then writing the deterministic and LLM-as-judge eval harnesses that prove those systems behave reliably. The scientific applications side (protein structure prediction, materials discovery) and developer-facing API products round out the portfolio, though your specific team placement determines which cluster dominates your calendar.

Skills & What's Expected

The skill that candidates most often misjudge is cloud infrastructure and deployment. It reads like a "nice to have" on paper, but the day-in-life data shows you debugging OOM errors on TPU slices by digging through cluster logs and adjusting batch sharding configs. If you can't reason about memory hierarchies on custom silicon, you'll bottleneck your own prototyping. The dual-expert bar on software engineering and ML/GenAI is the headline filter, sure. But the quiet killer is that math and statistics expectations here mean comfort with reward model calibration and self-revision techniques in RLHF, not just knowing how backprop works.

Levels & Career Growth

The job listing calls for 8+ years of software development and 5+ years of hands-on AI research, which maps most naturally to L5 or L6 entry. What separates those two levels isn't just years of experience. L5 owns complex multi-quarter projects and mentors junior engineers, while L6 requires setting technical direction across multiple teams and solving problems where nobody has scoped the solution yet. The promotion blocker from L5 to L6, based on the role descriptions, is demonstrating organizational influence beyond your own project. Excellent individual execution alone won't get you there.

Work Culture

The King's Cross office expects three days in-person (Tuesday through Thursday), with Monday and Friday flexible for remote deep work. Google has tightened remote tracking company-wide, and DeepMind teams tend to skew even more in-office because of the real-time collaboration with researchers (those Wednesday video calls reconciling eval metric definitions with the Zurich alignment team, for instance). Most engineers work roughly 10 AM to 6:30 PM and protect evenings, though on-call weeks and eval deadlines before launches stretch that. DeepMind's dedicated ethics team isn't decorative: the role data explicitly lists safety benchmark triage as a Monday morning activity, meaning responsible AI review is baked into your sprint cycle, not bolted on at the end.

Google DeepMind AI Engineer Compensation

Google's RSU grants can follow a front-loaded vesting schedule or vest evenly each year, and which structure you get shapes your real earnings trajectory. If your grant is front-loaded, the later years deliver noticeably less equity, and the data notes that refresh grants are common for high performers. That means your year 3 and 4 comp depends heavily on how DeepMind evaluates your contributions, not just your initial offer letter.

For negotiation, the offer notes make clear that RSU grant size and sign-on bonus are the primary levers, while base salary sits in a narrower band. Because DeepMind AI Engineers are building on Gemini infrastructure and optimizing for Ironwood TPUs (skills that Anthropic and OpenAI also desperately want), a competing offer from a frontier lab gives you concrete ammunition to push on equity. Don't fixate on any single comp component in isolation; pressure-test the full 4-year package, and ask explicitly about the sign-on bonus, because recruiters aren't always forthcoming about it.

Google DeepMind AI Engineer Interview Process

Seven rounds over roughly 5 weeks is the stated timeline, but the Presentation round is where DeepMind's process diverges from anything you'd see in a standard Google SWE loop. You're presenting a past project to a panel that includes researchers and engineers, and they will probe every technical decision you made. That round tests whether you can defend tradeoffs at the level of someone who both builds systems and understands the math behind them.

The most common rejection reasons from DeepMind all share a theme: depth gaps. Candidates get cut for shallow ML theory even when their code is clean, or for solid conceptual knowledge paired with an inability to design production ML systems with real operational considerations like monitoring, data drift, and rollback. The dedicated behavioral round also carries real weight. DeepMind's collaborative, mission-driven culture means a candidate who can't articulate how they navigate ambiguity or work across disciplines gives the committee a reason to pass.

Google DeepMind AI Engineer Interview Questions

from collections import deque
from dataclasses import dataclass, field
from typing import Dict, List


@dataclass
class Node:
    nxt: Dict[str, int] = field(default_factory=dict)
    fail: int = 0
    out: bool = False  # True if any pattern ends at this node


class AhoCorasick:
    def __init__(self, patterns: List[str]):
        self.nodes: List[Node] = [Node()]
        self._build_trie(patterns)
        self._build_failure_links()

    def _build_trie(self, patterns: List[str]) -> None:
        for p in patterns:
            if not p:
                # Empty phrase matches everywhere.
                self.nodes[0].out = True
                continue
            cur = 0
            for ch in p:
                if ch not in self.nodes[cur].nxt:
                    self.nodes[cur].nxt[ch] = len(self.nodes)
                    self.nodes.append(Node())
                cur = self.nodes[cur].nxt[ch]
            self.nodes[cur].out = True

    def _build_failure_links(self) -> None:
        q = deque()
        # Root's children fail to root.
        for ch, v in self.nodes[0].nxt.items():
            self.nodes[v].fail = 0
            q.append(v)

        while q:
            v = q.popleft()
            f = self.nodes[v].fail

            # Propagate outputs through failure links.
            if self.nodes[f].out:
                self.nodes[v].out = True

            for ch, u in self.nodes[v].nxt.items():
                # Find failure transition for (v, ch).
                ff = self.nodes[v].fail
                while ff != 0 and ch not in self.nodes[ff].nxt:
                    ff = self.nodes[ff].fail
                if ch in self.nodes[ff].nxt:
                    self.nodes[u].fail = self.nodes[ff].nxt[ch]
                else:
                    self.nodes[u].fail = 0
                q.append(u)

    def any_match(self, text: str) -> bool:
        # Streaming scan in O(|text|).
        state = 0
        # Early exit if empty pattern existed.
        if self.nodes[0].out:
            return True

        for ch in text:
            while state != 0 and ch not in self.nodes[state].nxt:
                state = self.nodes[state].fail
            if ch in self.nodes[state].nxt:
                state = self.nodes[state].nxt[ch]
            # If this state or any of its failure ancestors is terminal.
            if self.nodes[state].out:
                return True
        return False


def contains_any_banned_phrase(message: str, banned_phrases: List[str]) -> bool:
    """Return True if any banned phrase appears as a substring in message."""
    ac = AhoCorasick(banned_phrases)
    return ac.any_match(message)

import math
from dataclasses import dataclass
from typing import Dict, Iterable, Tuple, Optional

import torch
import torch.nn as nn
import torch.nn.functional as F


@dataclass
class LoopConfig:
    pad_token_id: int = 0
    max_grad_norm: float = 1.0
    use_amp: bool = True


def _shift_for_next_token(labels: torch.Tensor, pad_token_id: int) -> Tuple[torch.Tensor, torch.Tensor]:
    """Create inputs/targets for next-token prediction.

    labels: (B, T) token ids including padding.
    Returns:
      input_ids: (B, T-1)
      target_ids: (B, T-1)
    """
    if labels.ndim != 2:
        raise ValueError(f"labels must be (B,T), got {labels.shape}")
    if labels.size(1) < 2:
        raise ValueError("Sequence length must be >= 2")

    input_ids = labels[:, :-1].contiguous()
    target_ids = labels[:, 1:].contiguous()

    # Optional sanity: if input is pad, target should be pad too.
    # Do not enforce hard, but many pipelines satisfy this.
    return input_ids, target_ids


@torch.no_grad()
def _masked_token_accuracy(logits: torch.Tensor, targets: torch.Tensor, pad_token_id: int) -> torch.Tensor:
    """Compute accuracy excluding padding tokens.

    logits: (B, T, V)
    targets: (B, T)
    """
    preds = logits.argmax(dim=-1)
    mask = targets.ne(pad_token_id)
    correct = (preds.eq(targets) & mask).sum(dtype=torch.float32)
    denom = mask.sum(dtype=torch.float32).clamp_min(1.0)
    return correct / denom


def train_one_epoch(
    model: nn.Module,
    dataloader: Iterable[Dict[str, torch.Tensor]],
    optimizer: torch.optim.Optimizer,
    device: torch.device,
    cfg: LoopConfig,
    scaler: Optional[torch.cuda.amp.GradScaler] = None,
) -> Dict[str, float]:
    """Train for one epoch on a dataloader.

    Dataloader yields dict with key 'input_ids' or 'labels'. If only 'input_ids' is present,
    it is treated as the full sequence and is shifted to create targets.
    """
    model.train()

    if cfg.use_amp and scaler is None:
        scaler = torch.cuda.amp.GradScaler(enabled=(device.type == "cuda"))

    total_loss = 0.0
    total_acc = 0.0
    total_tokens = 0
    steps = 0

    for batch in dataloader:
        # Support either 'labels' or 'input_ids'.
        seq = batch.get("labels", batch.get("input_ids"))
        if seq is None:
            raise KeyError("Batch must contain 'labels' or 'input_ids'")

        seq = seq.to(device)
        input_ids, targets = _shift_for_next_token(seq, cfg.pad_token_id)

        optimizer.zero_grad(set_to_none=True)

        with torch.cuda.amp.autocast(enabled=(cfg.use_amp and device.type == "cuda")):
            outputs = model(input_ids)
            logits = outputs.logits if hasattr(outputs, "logits") else outputs
            # logits: (B, T-1, V)
            if logits.ndim != 3:
                raise ValueError(f"Expected logits (B,T,V), got {logits.shape}")

            vocab_size = logits.size(-1)
            loss = F.cross_entropy(
                logits.view(-1, vocab_size),
                targets.view(-1),
                ignore_index=cfg.pad_token_id,
                reduction="mean",
            )

        # Backprop with AMP.
        if scaler is not None and scaler.is_enabled():
            scaler.scale(loss).backward()
            # Unscale before clipping.
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.max_grad_norm)
            scaler.step(optimizer)
            scaler.update()
        else:
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.max_grad_norm)
            optimizer.step()

        # Metrics.
        with torch.no_grad():
            acc = _masked_token_accuracy(logits, targets, cfg.pad_token_id)
            token_mask = targets.ne(cfg.pad_token_id)
            n_tokens = int(token_mask.sum().item())

        total_loss += float(loss.item()) * max(n_tokens, 1)
        total_acc += float(acc.item()) * max(n_tokens, 1)
        total_tokens += max(n_tokens, 1)
        steps += 1

    mean_loss = total_loss / max(total_tokens, 1)
    mean_acc = total_acc / max(total_tokens, 1)
    ppl = float(math.exp(min(50.0, mean_loss)))

    return {"loss": mean_loss, "token_accuracy": mean_acc, "perplexity": ppl, "steps": steps}


@torch.no_grad()
def evaluate(
    model: nn.Module,
    dataloader: Iterable[Dict[str, torch.Tensor]],
    device: torch.device,
    cfg: LoopConfig,
) -> Dict[str, float]:
    model.eval()

    total_loss = 0.0
    total_acc = 0.0
    total_tokens = 0
    steps = 0

    for batch in dataloader:
        seq = batch.get("labels", batch.get("input_ids"))
        if seq is None:
            raise KeyError("Batch must contain 'labels' or 'input_ids'")

        seq = seq.to(device)
        input_ids, targets = _shift_for_next_token(seq, cfg.pad_token_id)

        outputs = model(input_ids)
        logits = outputs.logits if hasattr(outputs, "logits") else outputs
        vocab_size = logits.size(-1)

        loss = F.cross_entropy(
            logits.view(-1, vocab_size),
            targets.view(-1),
            ignore_index=cfg.pad_token_id,
            reduction="mean",
        )

        acc = _masked_token_accuracy(logits, targets, cfg.pad_token_id)
        token_mask = targets.ne(cfg.pad_token_id)
        n_tokens = int(token_mask.sum().item())

        total_loss += float(loss.item()) * max(n_tokens, 1)
        total_acc += float(acc.item()) * max(n_tokens, 1)
        total_tokens += max(n_tokens, 1)
        steps += 1

    mean_loss = total_loss / max(total_tokens, 1)
    mean_acc = total_acc / max(total_tokens, 1)
    ppl = float(math.exp(min(50.0, mean_loss)))

    return {"loss": mean_loss, "token_accuracy": mean_acc, "perplexity": ppl, "steps": steps}

The two heaviest areas, ML & Modeling and ML System Design, test overlapping muscles. A system design answer about a Gemini-scale serving pipeline falls flat if you can't explain why you'd choose a particular KV-cache strategy, and a modeling answer about RLHF reward hacking loses credibility if you ignore how that fix affects inference cost on TPU pods. The biggest prep mistake? Treating algorithm practice as your primary study block when the distribution clearly rewards deeper investment in modeling fundamentals and end-to-end ML system thinking.

Practice questions across all seven areas at datainterview.com/questions.

How to Prepare for Google DeepMind AI Engineer Interviews

DeepMind's public moves over the past year point toward tighter coupling between research and production. The Ironwood TPU codesigned AI stack pairs custom silicon with software optimized for it, which means AI Engineers on some teams write JAX code that compiles through XLA specifically for that hardware. On the product side, Atlas represents DeepMind's push into agentic AI with tool use and retrieval, while AI Studio appears to be how research prototypes get packaged for external developers.

The "why DeepMind?" answer that falls flat is any variation of "I want to work on AGI" or "AlphaFold inspired me." What separates strong answers, from what candidates report, is specificity about a technical constraint unique to this environment. Mention why JAX's functional paradigm matters when you control the compiler and the chip, or how RLHF pipelines change when the hardware team sits down the hall.

Try a Real Interview Question

from __future__ import annotations

import math
from typing import Iterable, Iterator, Tuple


def fit_temperature_scaling(
    data: Iterable[Tuple[float, int]],
    *,
    lr: float = 0.05,
    steps: int = 200,
    batch_passes: int = 1,
    init_T: float = 1.0,
    l2_theta: float = 0.0,
    clip_grad: float = 10.0,
    eps: float = 1e-12,
) -> float:
    """Fit a temperature $T>0$ to calibrate binary logits.

    Args:
        data: Iterable of (logit z, label y) pairs, where y is 0 or 1.
        lr: Learning rate for gradient descent on theta = log(T).
        steps: Number of optimization steps.
        batch_passes: Number of full passes over data per step (for noisy iterables set to 1).
        init_T: Initial temperature.
        l2_theta: Optional L2 penalty weight on theta.
        clip_grad: Clip absolute gradient to this value.
        eps: Small constant to keep T bounded away from zero.

    Returns:
        Learned temperature T as a float.
    """
    pass

from __future__ import annotations

import math
from typing import Iterable, Tuple


def _sigmoid(x: float) -> float:
    # Numerically stable sigmoid
    if x >= 0:
        z = math.exp(-x)
        return 1.0 / (1.0 + z)
    else:
        z = math.exp(x)
        return z / (1.0 + z)


def fit_temperature_scaling(
    data: Iterable[Tuple[float, int]],
    *,
    lr: float = 0.05,
    steps: int = 200,
    batch_passes: int = 1,
    init_T: float = 1.0,
    l2_theta: float = 0.0,
    clip_grad: float = 10.0,
    eps: float = 1e-12,
) -> float:
    """Fit a temperature T>0 to calibrate binary logits.

    Minimizes mean negative log-likelihood of sigmoid(z/T) for binary labels.
    Uses gradient descent on theta = log(T) for positivity.

    Args:
        data: Iterable of (logit z, label y) pairs, where y is 0 or 1.
        lr: Learning rate for gradient descent on theta = log(T).
        steps: Number of optimization steps.
        batch_passes: Number of full passes over data per step.
        init_T: Initial temperature.
        l2_theta: Optional L2 penalty weight on theta.
        clip_grad: Clip absolute gradient to this value.
        eps: Small constant to keep T bounded away from zero.

    Returns:
        Learned temperature T as a float.
    """

    if steps <= 0:
        raise ValueError("steps must be positive")
    if lr <= 0:
        raise ValueError("lr must be positive")
    if init_T <= 0:
        raise ValueError("init_T must be > 0")
    if batch_passes <= 0:
        raise ValueError("batch_passes must be positive")
    if clip_grad <= 0:
        raise ValueError("clip_grad must be positive")
    if eps <= 0:
        raise ValueError("eps must be positive")

    theta = math.log(max(init_T, eps))

    for _ in range(steps):
        grad_sum = 0.0
        n = 0

        T = math.exp(theta)
        invT = 1.0 / max(T, eps)

        # Full-batch gradient estimate over streaming data
        for _p in range(batch_passes):
            for z, y in data:
                if y != 0 and y != 1:
                    raise ValueError("label y must be 0 or 1")

                a = z * invT
                p_hat = _sigmoid(a)

                # dL/dtheta = (p - y) * d(a)/dtheta, with a = z * exp(-theta)
                # d(a)/dtheta = -a
                grad_sum += (p_hat - float(y)) * (-a)
                n += 1

        if n == 0:
            raise ValueError("data must contain at least one example")

        grad = grad_sum / n
        if l2_theta != 0.0:
            grad += l2_theta * theta

        if grad > clip_grad:
            grad = clip_grad
        elif grad < -clip_grad:
            grad = -clip_grad

        theta -= lr * grad

        # Keep theta in a reasonable range to avoid overflow/underflow
        if theta > 50.0:
            theta = 50.0
        elif theta < -50.0:
            theta = -50.0

    return float(math.exp(theta))

From candidate reports, DeepMind's algorithm rounds favor graph problems, dynamic programming, and numerical operations over string manipulation. The interviewer often cares less about getting to a working solution fast and more about whether you can articulate the mathematical structure behind your approach. Sharpen these patterns at datainterview.com/coding.

Test Your Readiness

Use your results to focus your remaining prep time, then practice targeted questions at datainterview.com/questions.

Presentation Round Prep

This round is unusual enough that it deserves dedicated preparation separate from your technical study. You're presenting a past project to a mixed panel of researchers and engineers who are likely to challenge your design choices with specific alternatives.

Pick a project where you made a non-obvious technical decision under real constraints, not your most impressive result, but your most defensible reasoning. Prepare to shift between the high-level architecture and low-level math (optimizer choice, learning rate schedule, data mix) in the same conversation. Researchers on the panel may have published on the exact alternatives you didn't pick.

Build a Weighted Study Plan

Your prep time should mirror the question distribution the widget above shows, not split evenly. The ML fundamentals and system design categories together dominate the interview mix, so front-load those in weeks one and two.

Algorithms and LLM/agent architectures (RLHF tradeoffs, evaluation methodology, tool-use patterns like those described in Atlas) deserve focused attention in week three. Save week four for the categories that carry disproportionate pass/fail weight relative to their frequency: drill JAX-based training loops from scratch without referencing docs, review TPU pod architecture and Vertex AI deployment concepts, and rehearse behavioral stories about project failures and team conflict. The hiring committee reads feedback from every round, so a weak behavioral signal can undermine otherwise strong technical scores.