Anthropic SDE interview is significantly different from that of ordinary major companies (such as Google and Meta): LLM Infra + AI Safety are fully integrated throughout the process. The interview does not test off-the-grid questions, nor does it focus on LeetCode problems, but focuses on distributed systems, reasoning performance optimization, concurrent scheduling, and security engineering implementation capabilities.
If you only brush LeetCode without understanding engineering issues such as large model inference, KV Cache, continuous batching, and security interception, it will be difficult to pass.
Anthropic SDE Interview Timeline Reference (Common Process)
- Resume delivery
- HR initial screening (30 minutes)
- Coding Challenge (90 minutes, 4 questions)
- Hiring Manager Call (45-60 minutes)
- Virtual Onsite (4 rounds, 45-60 minutes each)
- HR Feedback + Team Match
Round 1: HR interview (30 minutes)
On the surface, it is HR communication, but in reality, it is hidden assessment. After introducing myself at the beginning, the interviewer asked "Why did you choose Anthropic instead of OpenAI or Google DeepMind?" Many people would answer this question with "Your company is very good and I like it very much", but she was obviously waiting for an in-depth answer.
I made two points: First, I think Anthropic’s investment in Interpretability is real research, not a PR move - the work related to mechanism interpretability (such as the Superposition Hypothesis) released by Anthropic is the Safety research with the most engineering value that I have read so far; second, the idea of Constitutional AI makes me feel that Safety and Capability are not necessarily trade-off, but can be unified through system design.
She asked: "Then what do you think is the biggest risk in the future of AI?"
I did not answer in the direction of "AI will destroy humanity", but focused on the current actual engineering risks: the unpredictability of the model's behavior under distribution shift, and the exponential increase in safety audit costs after large-scale deployment. These two problems exist today in real systems and do not require AGI.
Experience: The core of this round is not Behavioral Questions, but to examine the depth of your understanding of Anthropic’s mission. If you lump them in with other AI companies, it will be very passive. It is recommended to carefully read their Core Views page and at least two Safety papers (Constitutional AI and Scaling and the Future of AI Safety are good starting points).
Round 2: Coding Challenge (90 minutes, 4 questions)
This round is completed asynchronously online. It is not a LeetCode test, but is closer to an engineering task.
The themes of the four questions all revolve around the actual scenarios of LLM services:
Question 1: Token Batching logic
Given a set of requests, each request has the number of prompt tokens and the number of expected output tokens, implement a batcher and try to improve throughput while satisfying GPU memory constraints and latency SLO. The core test point is whether you understand the difference between dynamic batching and continuous batching, and the memory waste problem caused by padding.
Question 2: Request scheduling simulation
Simulate a simplified inference scheduler to process requests of different priorities to ensure that P99 latency does not exceed the standard. The head-of-line blocking problem needs to be considered - the scenario where long requests block short requests is a classic pain point in actual LLM services.
Questions 3 & 4
Partial data processing, examining boundary condition processing related to stream processing logic and token statistics.
The difficulty itself is not high (Easy-Medium), but the topic description is very long, the boundary conditions are intensive, and the time is very tight. There are 4 questions in 90 minutes, with an average of less than 23 minutes per question, including question reading time.
Suggestion: Quickly scan all the questions first to determine the difficulty distribution, give priority to getting certain points, and don’t get stuck on one question.
Round 3: Hiring Manager Call (45 minutes)
This round is basically all technical, but it does not test formula derivation, but engineering judgment.
The Manager came up and asked: "Where are the main performance bottlenecks of LLM Inference? How do you analyze it systematically?"
The framework of my answer:
- Compute-bound vs Memory-bound: The Prefill phase is compute-bound (matrix multiplication intensive), and the Decode phase is memory-bandwidth-bound (only one token is generated at each step, but all KV Cache must be loaded). The optimization directions of these two stages are completely different and cannot use the same set of ideas.
- The core contradiction of KV Cache: KV Cache is the key to accelerating the Decode stage, but it occupies a large amount of HBM, which directly limits the concurrent batch size. The value of PagedAttention lies in changing the memory management of KV Cache from static pre-allocation to dynamic paging. It essentially draws on the virtual memory idea of the operating system and significantly reduces the fragmentation rate.
- Current hotspot direction: Speculative Decoding (use small models to guess and large models to verify, which can theoretically speed up without losing quality); MLA (Multi-head Latent Attention, used by DeepSeek, KV Cache has a high compression ratio); and Disaggregated Prefill/Decode (deploy Prefill and Decode to different nodes to optimize resource utilization respectively).
After listening, he asked a follow-up question: "In what scenarios does Speculative Decoding gain the most benefits, and in what scenarios is it almost useless?"
This is a good question. The most profitable scenario: the output token distribution is relatively concentrated and predictable (such as code completion, fixed format output). Scenarios with the least benefit: creative writing, high temperature output, small model guessing has a low hit rate, but introduces additional overhead.
Experience: This round tests your ability to flexibly switch between high-level abstractions (system architecture) and low-level details (memory bandwidth, matrix multiplication). Candidates who only understand theory but have no practical profiling experience will reveal their true colors during questioning.
Round 4: Virtual Onsite (4 rounds back-to-back)
Round 1: Coding & Optimization (60 minutes)
The background of the question is the request scheduling of Claude's inference service. The core issue is how to allocate GPU resources under concurrent requests to ensure the latency SLO of high-priority requests.
I first wrote a basic version based on priority queue, with time complexity O(n log n). The interviewer said, "Yes, now add a constraint: the memory is limited, a maximum of K requests can be processed in parallel at a time, and the requests can be preempted."
This way of adding constraints is typical of Anthropic - they don't want to see you write perfect code in one go, but they want to see the speed and quality of your iterative design solutions under new constraints.
I added preemption logic and used a min-heap to manage currently running requests. When a new high-priority request arrives and the resource is full, the running request with the lowest priority is preempted and its status is saved (simulating KV Cache checkpoint). The interviewer was very satisfied with this direction and asked how to control the checkpoint overhead. We discussed the trade-off of incremental saving vs. Full saving.
Round 2: System Design (60 minutes)
Topic：Design a scalable, low-latency LLM inference service for Claude, support multi-tenancy, and require content safety capabilities.
My design framework:
Client → API Gateway → Load Balancer
↓
Request Router (divided by request type: short prompt / long prompt / streaming)
↓
┌────────────────────────────┐
│ Safety Interceptor │ ← This is the part that Anthropic pays special attention to
└─────────────────────────────┘
↓
Dynamic Batcher (continuous batching)
↓
┌──────────────────────────┐
│Inference Workers │
│ (Tensor Parallel + │
│ Pipeline Parallel) │
└──────────────────────────┘
↓
KV Cache Manager (PagedAttention)The interviewer paused for a long time on "Safety Interceptor" and asked:
- How to do content safety detection without significantly increasing latency? My plan: The lightweight Safety Classifier runs in parallel with the main model. The latency of the Safety check is covered by the prefill time of the main model, which has minimal impact on the user's perceived latency.
- How to implement security isolation in a multi-tenant scenario? The core is the physical isolation of KV Cache (KV Cache of different tenants do not share memory pages), and the namespace isolation of the request scheduling layer to prevent side-channel information leakage.
The core feeling of this round: AI Safety is not a bonus point, but a basic point. If there is no place for Safety in your system design, points will be deducted directly.
Round 3: Project Deep Dive (60 minutes)
Digging deeper into the distributed inference projects I’ve done on my resume. The interviewer asked some very pointed questions:
"You said that performance degradation occurred after the system was expanded to 32 cards. How did you position it?"
This question examines real-life troubleshooting experience. I described the investigation process at that time:
- Use first
Nvidia-smiLooking at the GPU utilization distribution with DCGM, we found that the utilization of 4 cards was significantly lower than that of other cards. - Use PyTorch Profiler to profile these 4 cards individually and find that the waiting time for All-Reduce communication is abnormally long.
- After investigation, it was found that the nodes where these four cards are located cross the NVLink domain and use the PCIe path, and the bandwidth difference is 5 times.
- Solution: Replan the allocation of tensor parallel groups to ensure that GPUs in the same TP group are in the same NVLink fabric
The interviewer was very satisfied with this answer and asked, "If the hardware topology cannot be changed at that time, what workaround do you have?" - This is where the depth of the problem is truly examined. We discussed options such as adjusting TP size and switching to Sequence Parallelism to share communication pressure.
Experience: Project Deep Dive wheels are all about authenticity. The interviewer doesn't care how awesome your project is, but whether you have actually stepped on pitfalls and solved problems. If your answer does not include specific numbers, tool names, and failure processes, it means that you may be just a bystander on the project.
Round 4: Culture & Technical Values (45 minutes)
This is the most unique round of interviews at Anthropic, and there is basically no comparison with other companies.
The interviewer asked three questions, each of which requires a combination of technology and values:
“What is your biggest security concern with current LLM systems?”
I did not answer "the model generates harmful content" (too shallow), but focused on the safety blind spots at the system level: most current safety mechanisms act on the input/output layer of a single request, but progressive jailbreak in multiple rounds of dialogue, goal drift in long contexts, and tool use chain risks in agentic scenarios are all areas where existing mechanisms do not cover enough.
"In engineering practice, how do you balance iteration speed and safety compliance?"
My core point is: Safety should be an architectural decision, not an approval process. Specifically:
- Make Safety check a default component of the system instead of a checklist that is triggered manually before each release
- Use canary release + automated Safety regression test to replace manual review and automate Safety access control
- Incorporate safety indicators (such as refusal rate, harmful content rate) into SLO monitoring at the same level as latency and throughput
"What would you do if your Tech Lead asked you to skip testing a certain Safety detection module To meet the deadline?"
This question examines your true values under pressure. I said directly: I would refuse, but at the same time provide an alternative - change the safety test of the module from blocking to monitoring only, release it first, establish a quick rollback mechanism, and complete the test within 24 hours after release. This neither blocks the deadline nor runs naked.
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