DeepSeek AI Engineer Interview Guide

From hundreds of mock interviews with AI engineer candidates, one pattern keeps showing up: people prep for DeepSeek like it's a generic large-lab loop. It's not. About 45% of reported interview questions touch LLMs, transformers, or deep learning internals, and the role itself blurs the line between research and production in ways that catch even experienced candidates off guard.

DeepSeek AI Engineer Role

Success after year one means you've contributed something measurable to a real model release. Maybe you improved expert load balancing in DeepSeek-V3's MoE layers, or you built RL pipeline components for the R1 reasoning line using Group Relative Policy Optimization. "Research" and "production" aren't separate job families here. You debug a flaky NCCL communication backend on Wednesday morning and prototype a sparse attention variant in PyTorch by Friday afternoon.

A Typical Week

The thing that surprises candidates most is the infrastructure ownership. There's no platform team to throw problems to. You're pinning NCCL versions yourself, pairing with MLOps on DeepSeek-V3 serving pipelines, and extending Weights & Biases configs for MoE routing metrics. Friday research time is also real (not a "20% time" fiction): the team reads arxiv papers on speculative decoding and expert load balancing, then prototypes ideas that could land in the next training run.

Projects & Impact Areas

DeepSeek-V3's Mixture-of-Experts architecture is the flagship engineering surface, spanning expert routing logic, FP8 mixed-precision training, and aggressive cost optimization across the training pipeline. That efficiency focus connects directly to the open-source release strategy: engineers make model weights reproducible and community-deployable through HuggingFace, which means caring about quantization, distillation, and clean documentation alongside raw model quality. The R1 reasoning line is a distinct track where you build reinforcement learning pipelines using GRPO to improve chain-of-thought reasoning, a fundamentally different problem from the pretraining work on V3.

Skills & What's Expected

The most underrated skill is systems-level Python. Candidates assume "expert ML" means knowing transformer theory cold, but DeepSeek expects you to implement custom distributed training logic, write memory-aware data loaders, and debug GPU communication backends across a Ray cluster. Math and ML expertise are table stakes. On the other end, you won't spend much time building dashboards or presenting to business stakeholders, so if your strength is ML storytelling rather than ML implementation, recalibrate your prep.

Levels & Career Growth

The P6-to-P7 jump is where career velocity gets interesting. At a company still in rapid growth mode, that promotion can come fast if you ship a training improvement that makes it into a model release. P8+ roles are scarce and probably require owning an entire research direction (the MoE architecture track, the R1 reasoning pipeline), since the company simply doesn't have many of those seats yet.

Work Culture

DeepSeek's founder Liang Wenfeng came from the quant fund High-Flyer, and that hedge-fund DNA shapes daily life: small teams, high autonomy, relentless focus on efficiency over headcount. Ten-hour days are standard, weekend pushes happen around major model releases, and the role is hybrid with limited remote flexibility. Collaboration runs through Feishu and WeChat across tightly coupled research-engineering pods with almost no bureaucratic layering, and Liang's stated "we're done following" ethos means engineers are expected to propose original research directions, not just execute on a roadmap handed down from above.

DeepSeek AI Engineer Compensation

Equity follows a four-year vest with a one-year cliff, from what's publicly known. That cliff matters: if the schedule works like most private AI companies in China, leaving before month 13 means forfeiting your unvested grant entirely. Refresh grant policies aren't documented anywhere public, so during the offer stage, ask point-blank about refresh cadence, grant sizing relative to your initial package, and whether refreshes are tied to performance ratings or automatic.

Fresh graduate offers at DeepSeek reportedly range from 700,000 to 1.26 million CNY annually (including 14 months' salary), which signals that base salary carries real weight in the package. If you're holding a competing offer, push on the equity and sign-on components rather than base, since the reported salary bands for new grads suggest DeepSeek anchors base pay to structured ranges. Your strongest card is demonstrating specific depth in the areas DeepSeek actually ships, like MoE training, FP8 mixed precision, or RL-based reasoning pipelines, because that kind of specialization is harder to find than generic ML talent.

DeepSeek AI Engineer Interview Process

Six rounds across a roughly five-week window sounds standard, but the distribution of difficulty isn't even. The three middle technical rounds (Coding & Algorithms, ML & Modeling, System Design) carry the weight, and weak deep learning foundations are the rejection reason that shows up most often across those stages. Candidates who can discuss transformer theory at a surface level but stumble when asked to explain training dynamics, model evaluation tradeoffs, or how attention mechanisms actually behave in practice tend to get cut before they ever reach the final rounds.

The Bar Raiser round trips people up because it's not a soft behavioral conversation. The round description flags "abstract problem-solving" and "deep dives into your motivations," which in practice means a senior evaluator is testing your judgment and intellectual curiosity, not just checking STAR stories off a list. Walk in ready to articulate why specific AI problems interest you and how you'd approach open-ended challenges, because polished behavioral answers alone won't clear this gate if you can't demonstrate the kind of critical thinking DeepSeek screens for.

DeepSeek AI Engineer Interview Questions

from __future__ import annotations

from typing import Iterable, List, Tuple


def max_tokens_in_time_window(events: Iterable[Tuple[int, str, int]], T: int) -> int:
    """Return max sum of token_delta within any window of timestamps spanning at most T ms.

    Args:
        events: Iterable of (timestamp_ms, request_id, token_delta). request_id is unused.
        T: Non-negative window size in milliseconds.

    Returns:
        Maximum total token_delta across any contiguous set of events with max_ts - min_ts <= T.
    """
    if T < 0:
        raise ValueError("T must be non-negative")

    arr: List[Tuple[int, int]] = [(ts, delta) for ts, _rid, delta in events]
    if not arr:
        return 0

    arr.sort(key=lambda x: x[0])

    left = 0
    running = 0
    best = 0

    for right in range(len(arr)):
        ts_r, delta_r = arr[right]
        running += delta_r

        while arr[right][0] - arr[left][0] > T:
            running -= arr[left][1]
            left += 1

        if running > best:
            best = running

    return best


if __name__ == "__main__":
    sample = [
        (1050, "a", 10),
        (1000, "b", 7),
        (2200, "c", 5),
        (1600, "d", 20),
        (1700, "e", -3),
    ]
    print(max_tokens_in_time_window(sample, T=700))  # window [1000,1700] => 7+10+20-3 = 34

import math
from typing import Tuple

import torch


def scaled_dot_product_attention(
    Q: torch.Tensor,
    K: torch.Tensor,
    V: torch.Tensor,
    pad_mask: torch.Tensor | None = None,
    causal: bool = True,
    dropout_p: float = 0.0,
    training: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Scaled dot-product attention.

    Args:
        Q, K, V: (B, H, T, D)
        pad_mask: (B, T) bool or {0,1}, where True or 1 means valid token.
        causal: whether to apply causal mask (prevent attending to future positions).
        dropout_p: dropout probability on attention weights.
        training: whether to apply dropout.

    Returns:
        out: (B, H, T, D)
        attn: (B, H, T, T)
    """
    if Q.ndim != 4 or K.ndim != 4 or V.ndim != 4:
        raise ValueError("Q, K, V must be rank-4 tensors (B, H, T, D)")

    B, H, T, D = Q.shape
    if K.shape != (B, H, T, D) or V.shape != (B, H, T, D):
        raise ValueError("K and V must have the same shape as Q")

    # Compute logits: (B, H, T, T)
    scale = 1.0 / math.sqrt(D)
    logits = torch.matmul(Q, K.transpose(-1, -2)) * scale

    # Build and apply masks in logit space.
    # Use a large negative value compatible with dtype.
    neg_inf = torch.finfo(logits.dtype).min

    if causal:
        # Upper triangular (future positions) are invalid.
        # causal_mask: (T, T) True where allowed.
        causal_mask = torch.tril(torch.ones((T, T), device=logits.device, dtype=torch.bool))
        logits = logits.masked_fill(~causal_mask, neg_inf)

    if pad_mask is not None:
        if pad_mask.shape != (B, T):
            raise ValueError("pad_mask must be shape (B, T)")
        # Convert to bool where True means valid.
        valid = pad_mask.bool()
        # We mask keys (the attended-to positions). Broadcast to (B, 1, 1, T).
        key_valid = valid[:, None, None, :]
        logits = logits.masked_fill(~key_valid, neg_inf)

    # Softmax in fp32 for stability if needed.
    attn = torch.softmax(logits.float(), dim=-1).to(logits.dtype)

    if dropout_p > 0.0:
        attn = torch.dropout(attn, p=dropout_p, train=training)

    out = torch.matmul(attn, V)
    return out, attn


if __name__ == "__main__":
    torch.manual_seed(0)
    B, H, T, D = 2, 3, 5, 4
    Q = torch.randn(B, H, T, D)
    K = torch.randn(B, H, T, D)
    V = torch.randn(B, H, T, D)

    # Example pad mask: last two tokens padded in batch 0, none padded in batch 1.
    pad_mask = torch.tensor([[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]], dtype=torch.bool)

    out, attn = scaled_dot_product_attention(Q, K, V, pad_mask=pad_mask, causal=True)
    print(out.shape, attn.shape)

from __future__ import annotations

import torch
import torch.nn.functional as F


def masked_next_token_loss(
    logits: torch.Tensor,
    labels: torch.Tensor,
    pad_id: int,
    prompt_mask: torch.Tensor | None = None,
) -> torch.Tensor:
    """Compute next-token cross-entropy with correct shifting and masking.

    Args:
        logits: (B, T, V)
        labels: (B, T)
        pad_id: padding token id.
        prompt_mask: optional (B, T) bool where True means "this token is part of prompt".
            If provided, the loss ignores predicting tokens whose label position is prompt.

    Returns:
        Scalar loss normalized by count of valid supervised tokens.
    """
    if logits.ndim != 3 or labels.ndim != 2:
        raise ValueError("logits must be (B,T,V) and labels must be (B,T)")
    B, T, V = logits.shape
    if labels.shape != (B, T):
        raise ValueError("labels shape must match logits batch and time")

    # Next-token prediction: predict labels[:, 1:] from logits[:, :-1]
    pred = logits[:, :-1, :].contiguous()  # (B, T-1, V)
    target = labels[:, 1:].contiguous()    # (B, T-1)

    # Valid positions are those where target is not padding.
    valid = target.ne(pad_id)

    # Optional: ignore tokens that are part of the prompt at the target positions.
    if prompt_mask is not None:
        if prompt_mask.shape != (B, T):
            raise ValueError("prompt_mask must be (B, T)")
        valid = valid & (~prompt_mask[:, 1:])

    # Compute per-token CE without reduction.
    per_token = F.cross_entropy(
        pred.view(-1, V),
        target.view(-1),
        reduction="none",
    ).view(B, T - 1)

    # Normalize by the number of valid tokens to avoid padding-dependent loss scale.
    denom = valid.sum().clamp_min(1)
    loss = (per_token * valid.float()).sum() / denom
    return loss


if __name__ == "__main__":
    torch.manual_seed(0)
    B, T, V = 2, 6, 10
    pad_id = 0

    logits = torch.randn(B, T, V)
    labels = torch.tensor(
        [
            [5, 2, 3, 4, pad_id, pad_id],
            [7, 8, 1, 9, 2, 3],
        ]
    )

    # Example prompt mask, ignore supervision for first two target positions.
    prompt_mask = torch.tensor(
        [
            [True, True, False, False, False, False],
            [True, True, False, False, False, False],
        ]
    )

    loss = masked_next_token_loss(logits, labels, pad_id=pad_id, prompt_mask=prompt_mask)
    print(float(loss))

LLMs, ML modeling, and system design together account for about two-thirds of the interview, which tells you DeepSeek isn't screening for ML generalists. They're filtering for people who can reason about transformer training dynamics (MoE routing instability, FP8 precision tradeoffs, RLHF alternatives like GRPO) and then architect production systems around those constraints. The prep mistake most likely to sink you: treating coding and behavioral as equal time investments to the LLM-heavy rounds, when in reality a candidate who can't explain why DeepSeek-R1 skips supervised fine-tuning or debug a masked loss computation will wash out long before behavioral fit matters.

Practice with interview questions tailored to this breakdown at datainterview.com/questions.

How to Prepare for DeepSeek AI Engineer Interviews

DeepSeek's north star is achieving usable intelligence at production cost, and that priority shapes everything an AI Engineer touches. The company's technical reports detail architecture choices like Multi-head Latent Attention and Mixture-of-Experts routing that exist specifically to squeeze more capability out of fewer resources. Your day-to-day work orbits that same constraint: training efficiency, inference cost, and novel architectures that let a team reportedly under 200 people compete with labs ten times their size.

The most common "why DeepSeek" mistake isn't saying the wrong thing. It's staying too abstract. Candidates talk about open-source AI or cost efficiency in broad strokes, when interviewers want to hear you engage with how DeepSeek pursues those goals differently. Reference a specific architectural decision from their V3 or R1 model line and explain the tradeoff it implies. Founder Liang Wenfeng has described a culture of pursuing original research directions rather than replicating existing approaches, so frame your answer around a technical problem you'd want to solve here that you couldn't solve the same way at a larger, more resource-rich lab.

Try a Real Interview Question

from typing import Iterable, List, Sequence, Tuple


def fuse_retrieval_results(
    ranked_lists: Sequence[Sequence[Tuple[str, float]]],
    n: int,
) -> List[str]:
    """Fuse multiple ranked retrieval results.

    Args:
        ranked_lists: A sequence of ranked lists, each list contains (doc_id, score).
        n: Number of doc_ids to return.

    Returns:
        Top-n doc_ids after deduplication (max score) and sorting.
    """
    pass

from typing import Dict, List, Sequence, Tuple


def fuse_retrieval_results(
    ranked_lists: Sequence[Sequence[Tuple[str, float]]],
    n: int,
) -> List[str]:
    """Fuse multiple ranked retrieval results.

    Deduplicates by doc_id using the maximum score across all lists, then sorts by
    descending score with ties broken by lexicographically smaller doc_id.

    Args:
        ranked_lists: A sequence of ranked lists, each list contains (doc_id, score).
        n: Number of doc_ids to return.

    Returns:
        Top-n doc_ids after deduplication (max score) and sorting.

    Raises:
        ValueError: If n is negative.
    """
    if n < 0:
        raise ValueError("n must be non-negative")
    if n == 0:
        return []

    best: Dict[str, float] = {}
    for lst in ranked_lists:
        for doc_id, score in lst:
            prev = best.get(doc_id)
            if prev is None or score > prev:
                best[doc_id] = score

    items = sorted(best.items(), key=lambda x: (-x[1], x[0]))
    if n >= len(items):
        return [doc_id for doc_id, _ in items]
    return [doc_id for doc_id, _ in items[:n]]

DeepSeek's parent company is a quantitative hedge fund, and that DNA shows up in coding rounds. From what candidates report, problems tend to reward solutions that are both correct and memory-conscious, reflecting the kind of efficiency thinking you'd apply when processing massive training corpora or optimizing data pipelines for multi-node setups. Sharpen that muscle at datainterview.com/coding with a focus on string/array manipulation and dynamic programming problems.

Test Your Readiness

Run through the quiz, then practice explaining transformer internals and system design tradeoffs out loud at datainterview.com/questions. Verbal clarity on architecture decisions matters more here than memorized definitions.