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DeepSeek AI & ML Engineer interview questions

DeepSeek does not run a classic forward deployed program. It is a research-focused lab in China hiring deep learning researchers, core systems engineers, and full-stack developers. Our content covers the coding, ML, and systems depth its engineering loops test, which lean heavily toward model research and training infrastructure.

2 questions tagged16 concepts to master4 core topicsrole: AI & ML Engineer

The DeepSeek AI & ML Engineer interview process

Limited public data
No reliable public breakdown of DeepSeek's hiring loop was found. By role type, expect a standard lab process (resume screen → coding test → ML-fundamentals interview → system design), but we can't confirm the specifics.

Compiled from our research and publicly available information (candidate reports and company interview guides). Interview loops change and are continuously iterated, and they vary by team, level, and region. Treat this as directional preparation, not an official spec, and confirm the exact rounds with your recruiter or hiring point of contact.

DeepSeek AI & ML Engineer salary

What we can trace, labelled by where it came from. We publish a band only where there is a source behind it, so some of this page is a gap rather than a number.

NO TRACEABLE BAND

We have not found a compensation figure for this role at DeepSeek that we can trace to an employer posting or a public aggregator. Rather than publish an estimate, we are naming the gap. Their careers page is the authority, and postings in some jurisdictions are required to state a range.

HIRING FROM INDIA
No India hiring route we can trace

We have not found an India hiring route for this role at this company. That is a statement about what we could verify, not proof that none exists.

Rather than publish an India estimate we cannot defend, we are naming the gap. Their careers page is the authority; if you find an India req, we would like to know.

Full method, US bands by level, and the three India tiers side by side are in the FDE salary guide.

Questions modeled on DeepSeek loops

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More from the tracks DeepSeek's loop tests

The highest-signal questions across DeepSeek's core tracks.

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Go deeper on the topics DeepSeek's loop tests

The tracks that map to a DeepSeek AI & ML Engineer loop, ordered easy to hard.

LLM & GenAI59 →ML & Data Science70 →ML Infrastructure & GPUs46 →Coding & DSA94 →

The concepts DeepSeek's AI & ML Engineer loop assumes you know

The vocabulary and mental models behind DeepSeek's questions, from our curriculum. Start with the foundations free; the deeper, interview-defining ideas are part of premium.

FOUNDATIONS OF LLMS & GENAI

Foundational
Tokenization & TokensA language model does not read characters or words. It reads tokens: sub-word chunks produced by a tokenizer, each mapped to an integer the model embeds. Tokens are the unit of the context window and of billing, and the way text splits into them explains a surprising number of model quirks, which is why almost every loop opens here.
Foundational
The Context WindowThe context window is the fixed number of tokens a language model can attend to at once, and input and output share that same budget. Understanding it is what separates engineers who can size a prompt, control cost and latency, and decide when to reach for RAG from those who just paste everything in and hope.
Foundational
Embeddings & Vector RepresentationsAn embedding turns a piece of text into a list of numbers positioned so that similar meanings land near each other in space, which lets you search by meaning instead of by keyword. Embeddings are the engine under RAG, semantic search, clustering, and deduplication, so FDE loops expect you to explain cosine similarity and the pitfalls that quietly break a vector index.
Advanced🔒 Premium
LoRA and Parameter-Efficient Fine-tuningFull fine-tuning updates every weight in a model, which is expensive to train and produces a full-size checkpoint per task. LoRA freezes the base model and trains small low-rank adapter matrices instead, giving tiny swappable checkpoints; QLoRA adds a quantized frozen base so the whole thing fits on a single GPU. FDE loops probe it because it is how you adapt a model on a customer's data without their budget or their hardware blowing up.

EVALUATION & ML FOUNDATIONS

CoreSign in
Information Theory for ML: Entropy, Cross-Entropy, KL and PerplexityFour quantities from information theory keep showing up in ML: entropy measures the average surprise in a distribution, cross-entropy is the loss that trains classifiers and language models, KL divergence measures how far one distribution sits from another, and perplexity is the intuitive branching-factor view of a language model's loss. Knowing where each appears separates people who tuned a loss function from people who only imported one.
Foundational
Precision, Recall and F1Precision asks how many of your positive predictions were right; recall asks how many of the real positives you caught. They trade off against each other, F1 is their harmonic mean, and accuracy lies to you the moment the classes are imbalanced.
Foundational
Gradient Descent & Learning RateGradient descent is how almost every model learns: compute the slope of the loss with respect to the weights, then step the weights a little in the downhill direction. The learning rate sets the step size, and it is the single most consequential knob. Too small and training crawls; too large and it overshoots and diverges.
Advanced🔒 Premium
Offline vs Online EvaluationOffline evaluation scores a change against a fixed golden set: fast, cheap, repeatable, and runnable in CI before anything ships. Online evaluation measures the change on real traffic and real users, usually via A/B, and is the only true read on impact. The two are not interchangeable: offline gains routinely fail to hold online because of distribution shift and metric gaming. The discipline FDE loops test is using offline to gate and online to confirm.

ML INFRASTRUCTURE & SERVING

CoreSign in
GPU Memory and VRAMVRAM is the budget that decides which models you can actually run. It is spent on three things: model weights, the KV cache, and activations. Knowing the back-of-envelope arithmetic (a 7B model at fp16 is roughly 14GB of weights) is what separates a candidate who has deployed an LLM from one who has only read about it.
CoreSign in
QuantizationQuantization stores model weights (and sometimes activations) in fewer bits, fp16 down to int8 or 4-bit, which cuts memory and speeds inference. The quality hit is usually small at int8 and larger at 4-bit. Knowing post-training quantization versus quantization-aware training, and when each is acceptable, is standard FDE interview ground.
CoreSign in
Knowledge DistillationDistillation trains a small student model to mimic a large teacher, learning from the teacher's full output distribution rather than just hard labels. The soft targets carry extra signal about how the teacher 'thinks', so the student keeps much of the quality at a fraction of the size and latency. Knowing when distillation beats quantization or pruning is standard FDE ground when you have a latency or cost budget to hit.
Advanced🔒 Premium
Continuous BatchingStatic batching runs a fixed group of requests to completion together, so a batch of one short reply and one long reply makes the GPU idle while it waits on the longest. Continuous batching adds and evicts sequences from the running batch every decode step, keeping the GPU saturated and multiplying throughput. It is the scheduling trick at the heart of vLLM and every modern LLM serving stack.

CODING & ENGINEERING CRAFT

Foundational
Parsing Messy, Real-World DataCustomer files are dirty: inconsistent quoting, missing headers, junk rows, encodings that lie. The job is to parse defensively, skip and log bad rows instead of aborting the whole batch, and keep parsing pure and separate from business logic so it stays testable and deterministic. This is most of what early FDE data-ingestion work actually is.
Foundational
Big-O That Actually MattersOn a deployment, Big-O is not a whiteboard puzzle; it is the one calculation that tells you whether the customer's data fits in the approach you picked. The skill is spotting the term that dominates at their scale, knowing when brute force dies and you need an index or ANN, and recognizing when constant factors and memory decide the outcome instead of the exponent.
CoreSign in
Testability and Dependency InjectionCode that reaches out to the clock, the network, the filesystem, or a random generator cannot be tested deterministically, because its output depends on the world. The fix is to separate pure logic from side effects and inject the things that touch the world (the clock, I/O, randomness) so a test can pass fakes. When you inherit untestable code, pin its current behavior with a characterization test first, then refactor under that net.
CoreSign in
Streaming and BackpressureStreaming processes data one chunk at a time so memory stays flat no matter how big the input is. The moment a producer outruns its consumer, you need backpressure: a bounded buffer that makes the producer wait instead of piling unbounded work into memory. In Python this is generators and chunked reads for the streaming half, and a bounded queue (or a blocking put) for the backpressure half. Get it wrong and a 50 GB file or a fast upstream OOMs the box.
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Where to apply, and official DeepSeek resources

Straight from DeepSeek: open roles and the company's own hiring guidance. Prep here, then apply there.

ApplyDeepSeek recruiting (Chinese)

External links to DeepSeek's own pages. Roles and processes change; always confirm on the official site.

ABOUT THE ROLE
What is a forward deployed engineer?What FDE roles payWho else is hiringThe skills FDE loops test
DEEPSEEK INTERVIEW FAQ
Does DeepSeek hire Forward Deployed Engineers?▲

No. DeepSeek is a research lab and does not run a classic forward deployed or customer-facing engineering program. It hires deep learning researchers, systems engineers, and developers.

What does the DeepSeek interview test?▼
What is the DeepSeek researcher salary?▼

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