I've always heard. Anthropic The MLE bar is not only high, but also the requirement for culture fit is almost the most demanding, and after experiencing it personally, it's really a double pull of "technology + values". In June, I successfully got the opportunity on LinkedIn network, and when I went to Virtual Onsite, I was even a bit "depressed". Now the process is sort of settled, to review a bit, but also by the way to the back to prepare for Anthropic students to leave some dry goods.
Anthropic Interview Overview
| Link | Date (2025) | Focus of inspection |
| Initial Screening | June 10 | preliminary screening |
| Technical Phone Interview | July 5 | Coding (Programming Skills), ML Theory (Machine Learning Theory) |
| Virtual Onsite | July 20 | Coding (Programming ability ×2), and System Design (system design). Project Discussion (item for discussion). Culture Fit (Cultural fit) |
| HR Feedback + Leadership Follow-up | August 5 + August 10 | Team Match (Team Match) |
Anthropic Interview Process Revealed
Phone Interview - Coding
The topic was to implement a customized attention for a small-scale LLM, and I wrote a basic implementation of scaled dot-product, which I thought was okay. I didn't expect the interviewer to follow up directly with "If memory resources are limited, how do you plan to further optimize the memory footprint?"
This question is a bit stuck, I was still stuck in the code implementation level. At this time, the senior voice reminds: "You can consider KV cache compression or chunking." I immediately picked up the idea and added the optimization on low-rank decomposition and chunking mechanism, and the interviewer obviously nodded. If it wasn't for the reminder, I might have lost points here.
Phone Interview - ML Theory
The second question was RLHF, focusing on the safety-first perspective of Anthropic. I started by comparing the textbook: pretraining → reward model → PPO.
As a result, the follow-up came: "Then how to prevent the reward model from overfitting?" I instinctively wanted to answer regularization, but my senior immediately prompted, "Remember to add human feedback pipeline and data diversity." I then told them about the diversity of data distribution, regular resampling, and the comparison between reward model and baseline model, so as to make the answer not so thin. When I thought about it later, what Anthropic cares a lot about is the engineering feasibility + safety assurance, which can't be achieved by relying on theoretical terminology alone.
VO - Coding (first round)
Optimizing Claude-like models for inference speed on mobile. I directly dumped the quantization, distillation of the two conventional programs. I didn't expect the interviewer to push right away: "How do you manage the KV cache in a low latency scenario?"
I froze for a few seconds and my mind went blank. My senior reminded me to mention cache reuse and trimming, so I hurriedly added that I could reduce latency by dynamically trimming the cache length, reusing historical key-values, and storing in tiers. This idea completely saved me, otherwise I guess I would have to hang up in this round.
VO - Coding (second round)
Write a function to detect and mitigate bias in the LLM output, strictly adhering to Anthropic's guideline. i initially wanted to use regular to detect specific keywords, but saying it out loud made me feel it was too sketchy.
Assist immediately prompted: "Anthropic especially emphasizes explainability, to say pipeline and user context." So I switched my thoughts and answered bias detection pipeline → classifier scoring → mitigation So I switched my thinking and answered bias detection pipeline → classifier scoring → mitigation module (e.g., replacement, explanation hints), and at the same time dynamically adjusted the threshold according to the user profile. So I switched my mindset and answered bias detection pipeline → classifier score → mitigation module (e.g. replace, explain hints), while dynamically adjusting the threshold according to user profile.
VO - System Design
The topic is to design a large-scale distributed training system. I can talk about model parallelism, data parallelism, and pipeline parallelism. But the interviewer followed up by asking, "What would you do to make sure that the safety constraint is still valid when scaling?"
This is now really an Anthropic featured question. All I could think about was checkpoint and fault tolerance. My senior immediately reminded me in his voice, "Think of safety constraints as a part of the pipeline." I immediately started to talk about filtering sensitive samples in the data preprocessing stage, injecting safety preference in the RLHF stage, and adding deviation detection in the monitoring system. By combining security and distributed systems in this way, the answer is immediately more complete.
VO - Culture Fit
The last part of the interview was behavioral. The interviewer asked, "Tell me about a time when you made a safety-related decision in a project." The example I initially prepared was too general, and I could only say, "We follow the norms...". I could only say "we follow the code".
The senior immediately prompted, "It's about tradeoffs and team communication." I immediately adjusted my answer, describing a project where we had a conflict between performance and security: on one hand, the customer wanted to be fast, on the other hand, the security standards were very rigid. In the end, I led the team to go live with security first, then optimize performance over time, and document the rationale for decision-making to ensure that future scaling is not risky. This version is now in line with Anthropic's values.
On the whole, Anthropic's questions are really technical + value-bound, and each question is very detailed. I was more technical in my preparation, but with the voice-assisted reminders, I didn't miss a lot of key points, especially when it comes to security constraints, bias mitigation, team decision-making, and those topics. I feel that without Programhelp's assistance, I would have failed in two or three rounds.
Anthropic MLE High Bar Interview Review | Every cardinal point could have been a hang-up, and the assists kept me afloat
To be honest, I didn't make it to the team match by myself, but with programhelp at key chokepoints. Remote voice assistance at critical choke points. I was able to complete my answers by being instantly reminded of points like attention optimization, KV cache management, and security constraint injection when the interviewer pushed.
Anthropic is a company that emphasizes technical depth + security orientation + cultural fit, so it's really hard to remedy the situation once you drop the ball.
Anthropic is a company that emphasizes technical depth, security orientation, and cultural fit, and it's really hard to fix it once you've dropped the ball.
If you're preparing for a similarly difficult interview (be it Anthropic, OpenAI, or Google DeepMind), don't go it alone - Programhelp's remote assist mode will give you a few pointers at the most vulnerable moments, so you can give a thorough and to-the-point answer that will stand up to even the most demanding of examiners.
