DeepSeek AI Researcher Interview Guide

Most candidates from Western labs walk into a DeepSeek interview expecting a standard research scientist loop. They're wrong. From what we see in mock interviews, the biggest shock is that DeepSeek doesn't separate "the person who writes the paper" from "the person who writes the CUDA kernel." If you can't go from a mathematical derivation to production distributed training code, you'll struggle to make it through the technical rounds.

DeepSeek AI Researcher Role

Success after year one at DeepSeek means your name is on a shipped model, not just a paper. You'll work directly on the architecture and training infrastructure behind models like DeepSeek-V3 (their MoE flagship) or DeepSeek-R1 (which uses reinforcement learning to elicit reasoning behavior without relying on supervised fine-tuning). The bar is a tangible contribution to the next model generation's quality-per-FLOP ratio, whether that's a new attention variant, a better load-balancing scheme for mixture-of-experts routing, or a training stability fix that saves significant wasted compute.

A Typical Week

The split that catches people off guard is how much infrastructure work falls on your plate. You're personally configuring multi-node GPU jobs, debugging NCCL hangs from overnight runs, and writing fault-tolerance wrappers. Coding and research blur into each other: Tuesday you're implementing a KV-cache compression kernel in PyTorch, and by Thursday you're writing up the failure modes you discovered in long-context generation.

Projects & Impact Areas

DeepSeek-V3's architecture innovations (Multi-head Latent Attention, auxiliary-loss-free MoE load balancing, FP8 mixed-precision training) aren't just published results. They're the production backbone, and new hires inherit and extend them. Alongside that core efficiency work, DeepSeek-R1 opened a second front using RL-based training to improve reasoning capabilities, which means researchers here bounce between training infrastructure problems and fundamental questions about how reasoning emerges in large models. Next-gen efforts in multimodal models and longer-context architectures are where most new headcount is pointed.

Skills & What's Expected

Expert-level math and ML are table stakes, not differentiators. What actually separates hires from rejects is the software engineering dimension: can you translate a paper's equation 7 into a correct, efficient PyTorch implementation and then scale it across a large GPU cluster? The underrated skill is technical writing. DeepSeek publishes detailed technical reports for their major releases and maintains internal experiment write-ups with fast turnaround, so if you can't explain why your KV-cache compression failed on long sequences in clear prose, you're missing a real part of the job.

Levels & Career Growth

The gap between P6 and P7 isn't years of experience; it's whether you can own a research direction versus execute within one someone else defined. Jumping to P8 is the hardest move because it requires shaping multi-quarter strategy across model generations, and at a compact company with roughly 150 to 200 people, Staff slots are scarce. What blocks promotion isn't usually technical ability; it's failing to connect your research to a shipped model.

Work Culture

DeepSeek operates out of Hangzhou (with some roles tied to Beijing), fully in-office, no remote option from what candidates report. Expect intensity: schedules during push periods before major releases stretch well beyond standard hours, overnight training jobs are common, and junior researchers are expected to independently drive experiments within weeks of joining. The upside is that Liang Wenfeng's quant-fund background means decisions happen fast, hierarchy is flat, and nobody cares about your pedigree if your ablation results are compelling. The open-source commitment is genuine (their Hugging Face repos include full training configs, not just weights), so your work gets seen by the global research community almost immediately.

DeepSeek AI Researcher Compensation

The widget shows stock grant values at P8 and P9, but no equity appears at P6 or P7. That split matters. If you're coming in at the mid or senior level, your comp is almost entirely cash plus performance bonus, so negotiate your guaranteed first-year bonus hard, because there's no equity upside to compensate for a soft base. For P8+, the stock grants are substantial, but you should ask in your recruiter screen exactly what instrument they represent (restricted stock, phantom equity, profit-sharing) and whether the vesting schedule is linear or back-loaded. A 4-year vest with a 1-year cliff is the stated structure, but back-loading changes the math dramatically.

The single biggest lever most candidates miss isn't cash at all. DeepSeek's key responsibilities at every level emphasize publishing at top venues and staying at the research frontier, so negotiating for dedicated compute budget, conference travel, and explicit publication terms gives you career capital that compounds long after a sign-on bonus is spent. Get any such commitments in writing. Beijing's cost of living is lower than San Francisco or London, which means the purchasing power of these packages stretches further than the raw USD numbers suggest.

DeepSeek AI Researcher Interview Process

The decision process is unusually flat. From what candidates report, there's no layered hiring committee like you'd find at Google DeepMind or Meta FAIR. Instead, the interviewers from your technical rounds carry outsized weight in the final call, which means a single weak round is harder to offset with strength elsewhere. Six rounds across four weeks sounds standard, but the lack of a committee "averaging" step makes each conversation higher stakes than it feels in the moment.

Most candidates who get cut share the same failure mode: they can describe results but fall apart on follow-ups about ablations, failure cases, and what they'd change now. The System Design round also trips up people from product-focused ML teams, because it centers on distributed training and serving infrastructure (parallelism strategies, quantization tradeoffs, memory budgeting) rather than end-user system prompts like "design a news feed ranker." If your experience is mostly inference-side or application-layer, spend extra time on training-loop mechanics before you walk in.

DeepSeek AI Researcher Interview Questions

from __future__ import annotations

from collections import Counter, deque
import heapq
from typing import Deque, Dict, Iterable, List, Tuple


def top_k_frequent_last_n(tokens: Iterable[int], N: int, k: int) -> List[int]:
    """Return top-k most frequent token ids in the last N tokens.

    Tie-break: smaller token id ranks higher.

    Time: O(N + U log k) where U is unique ids in the window.
    Space: O(U + N) due to window storage.
    """
    if N <= 0 or k <= 0:
        return []

    # Build the last-N window (stream-safe, but stores last N).
    window: Deque[int] = deque(maxlen=N)
    for t in tokens:
        window.append(t)

    # Count frequencies within the window.
    freq: Counter[int] = Counter(window)
    if not freq:
        return []

    # Maintain a min-heap of size k with worst element at the top.
    # We want highest (count, -id) when using a min-heap, so the "worst"
    # is smallest count, and for equal counts, largest id.
    heap: List[Tuple[int, int]] = []  # (count, -id)

    for token_id, count in freq.items():
        entry = (count, -token_id)
        if len(heap) < k:
            heapq.heappush(heap, entry)
        else:
            # Replace if better than current worst.
            if entry > heap[0]:
                heapq.heapreplace(heap, entry)

    # heap contains up to k items, but unordered. Sort to output by
    # descending count, then ascending token id.
    top = sorted(heap, key=lambda x: (-x[0], -x[1]))
    return [-neg_id for _, neg_id in top]


if __name__ == "__main__":
    # Example
    tokens = [5, 1, 5, 2, 2, 2, 3, 1, 1]
    print(top_k_frequent_last_n(tokens, N=7, k=2))  # expected [2, 1]

The distribution skews toward questions where you must hold a theoretical idea and its implementation consequences in your head simultaneously. LLM-focused and math/optimization questions frequently compound in a single exchange: the sample questions show scenarios like tuning a KL penalty in an SFT objective, then immediately probing whether you understand the optimization dynamics that follow. That blend rewards candidates who've actually trained and debugged transformer models, not just read about them.

The prep mistake most candidates make isn't neglecting any one area. It's preparing each area in isolation. DeepSeek's interview weaves probability into evaluation questions, optimization into system design prompts, and coding into ML primitives, so drilling topics as separate buckets leaves you unprepared for the crossover pressure you'll face in real rounds.

Sharpen that crossover fluency with realistic practice problems at datainterview.com/questions.

How to Prepare for DeepSeek AI Researcher Interviews

DeepSeek's north star is achieving usable intelligence at production cost, which means every research hire is evaluated through the lens of compute efficiency. The company prioritizes reasoning stability, long-context handling, and inference efficiency over brute-force scaling, and Liang Wenfeng has said publicly that DeepSeek is "done following," choosing architectural innovation over simply buying more GPUs. Your day-to-day will orbit these priorities.

Most candidates fumble the "why DeepSeek" question by talking about open-source AI in general terms. What separates you is showing a specific, informed opinion on the company's architectural choices, backed by reading their technical reports and being ready to discuss what you'd explore next. Stanford's analysis of DeepSeek's disruption gives useful context on why their cost-efficiency approach matters at an industry level, but your interviewers will care far more about whether you can reason through the tradeoffs yourself.

Try a Real Interview Question

from typing import Tuple
import numpy as np


def rmsnorm_forward_backward(
    X: np.ndarray,
    g: np.ndarray,
    dY: np.ndarray,
    eps: float = 1e-6,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Compute RMSNorm forward output Y and gradients dX, dg.

    Args:
        X: Input array of shape (B, T, D).
        g: Scale vector of shape (D,).
        dY: Upstream gradient of shape (B, T, D).
        eps: Small constant for numerical stability.

    Returns:
        Y: RMSNorm output of shape (B, T, D).
        dX: Gradient with respect to X of shape (B, T, D).
        dg: Gradient with respect to g of shape (D,).
    """
    pass

from typing import Tuple
import numpy as np


def rmsnorm_forward_backward(
    X: np.ndarray,
    g: np.ndarray,
    dY: np.ndarray,
    eps: float = 1e-6,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Compute RMSNorm forward output Y and gradients dX, dg.

    Args:
        X: Input array of shape (B, T, D).
        g: Scale vector of shape (D,).
        dY: Upstream gradient of shape (B, T, D).
        eps: Small constant for numerical stability.

    Returns:
        Y: RMSNorm output of shape (B, T, D).
        dX: Gradient with respect to X of shape (B, T, D).
        dg: Gradient with respect to g of shape (D,).
    """
    if X.ndim != 3:
        raise ValueError(f"X must have shape (B,T,D), got {X.shape}")
    if dY.shape != X.shape:
        raise ValueError(f"dY must have same shape as X, got {dY.shape} vs {X.shape}")
    if g.ndim != 1:
        raise ValueError(f"g must have shape (D,), got {g.shape}")
    B, T, D = X.shape
    if g.shape[0] != D:
        raise ValueError(f"g must have length D={D}, got {g.shape[0]}")
    if eps <= 0:
        raise ValueError("eps must be > 0")

    # Forward
    ms = np.mean(X * X, axis=-1, keepdims=True)  # (B,T,1)
    inv_rms = 1.0 / np.sqrt(ms + eps)  # (B,T,1)
    X_hat = X * inv_rms  # (B,T,D)
    Y = X_hat * g.reshape(1, 1, D)

    # Backward
    # dY = dL/dY
    # Y = g * X_hat
    dX_hat = dY * g.reshape(1, 1, D)  # (B,T,D)
    dg = np.sum(dY * X_hat, axis=(0, 1))  # (D,)

    # X_hat = X * inv_rms
    # inv_rms = (mean(X^2) + eps)^(-1/2)
    # For each vector x in R^D:
    # dL/dx = inv_rms * dL/dx_hat + x * (dL/dinv_rms)
    # where dL/dinv_rms = sum_i dL/dx_hat_i * x_i
    dot = np.sum(dX_hat * X, axis=-1, keepdims=True)  # (B,T,1)

    # inv_rms depends on ms = mean(x^2)
    # dinv_rms/dms = -1/2 * (ms+eps)^(-3/2) = -0.5 * inv_rms^3
    dinv_dms = -0.5 * (inv_rms ** 3)  # (B,T,1)

    # dL/dms = dL/dinv_rms * dinv_rms/dms
    dms = dot * dinv_dms  # (B,T,1)

    # ms = (1/D) * sum_i x_i^2
    # dms/dx_i = (2/D) * x_i
    dX_from_ms = dms * (2.0 / D) * X  # (B,T,D)

    dX = dX_hat * inv_rms + dX_from_ms
    return Y, dX, dg

DeepSeek's research model, where the same person goes from math derivation to distributed training code, means their coding problems tend to blend numerical reasoning with implementation. The company's focus areas (inference efficiency, cost predictability) reward candidates who can write performant numerical code, not just pass algorithmic puzzles. Sharpen this skill at datainterview.com/coding, prioritizing ML primitives and numerical computing problems.

Test Your Readiness

Identify your weak spots, then close them with focused reps at datainterview.com/questions.