Anthropic 2026 面试全流程攻略：OA → Phone → Onsite 真实面经汇总
Anthropic（人类学）作为 AI 安全领域的领军企业，2026 年面试以AI 系统深度和安全对齐思维为核心。面试官特别看重你对 LLM 底层原理、Inference 优化和 AI Safety 的理解。面试难度：★★★★★（技术深度 + AI Safety 双重考察）
2026 年面试流程时间线：
- 📝 OA（HackerRank）：1-2 道编程题，算法 + ML 基础
- 📞 Phone Screen：45 分钟，1 道编程 + AI 领域讨论
- 💻 Onsite：3-4 轮（Coding + System Design + AI Safety + BQ）
一、OA 阶段：在线笔试
Anthropic 的 OA 通常在 HackerRank 上进行，1-2 道编程题，偏算法和 ML 基础。
高频题：A* 搜索算法
import heapq
def a_star(grid, start, end, heuristic=None):
"""
A* 路径搜索算法
时间: O(V log V), 空间: O(V)
"""
if heuristic is None:
heuristic = lambda a, b: abs(a[0]-b[0]) + abs(a[1]-b[1])
rows, cols = len(grid), len(grid[0])
open_set = [(heuristic(start, end), 0, start)]
came_from = {}
g_score = {start: 0}
closed_set = set()
while open_set:
_, _, current = heapq.heappop(open_set)
if current == end:
path = []
while current in came_from:
path.append(current)
current = came_from[current]
path.append(start)
return path[::-1]
if current in closed_set:
continue
closed_set.add(current)
for dr, dc in [(-1,0),(1,0),(0,-1),(0,1)]:
nr, nc = current[0]+dr, current[1]+dc
neighbor = (nr, nc)
if 0 <= nr < rows and 0 <= nc < cols and grid[nr][nc] == 0:
tentative_g = g_score[current] + 1
if tentative_g < g_score.get(neighbor, float('inf')):
came_from[neighbor] = current
g_score[neighbor] = tentative_g
f = tentative_g + heuristic(neighbor, end)
heapq.heappush(open_set, (f, tentative_g, neighbor))
return None # 无解二、Phone Screen 技术电面
Phone 轮 45 分钟，1 道编程题 + AI 领域讨论。编程题难度 Medium，但讨论环节可能涉及 Transformer 架构、Attention 机制等。
示例：Transformer Attention 实现
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, dim, num_heads, dropout=0.1):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.q_proj = nn.Linear(dim, dim)
self.k_proj = nn.Linear(dim, dim)
self.v_proj = nn.Linear(dim, dim)
self.out_proj = nn.Linear(dim, dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask=None):
"""
Multi-Head Self-Attention
x: (batch, seq_len, dim)
"""
batch_size, seq_len, _ = x.shape
# 投影到 Q, K, V
q = self.q_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
k = self.k_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
v = self.v_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
# 转置: (batch, heads, seq_len, head_dim)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
# Attention scores
scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale
if mask is not None:
scores = scores.masked_fill(mask == 0, float('-inf'))
attn_weights = F.softmax(scores, dim=-1)
attn_weights = self.dropout(attn_weights)
# Apply attention to values
out = torch.matmul(attn_weights, v)
out = out.transpose(1, 2).reshape(batch_size, seq_len, -1)
return self.out_proj(out)三、Onsite – System Design & AI 深度考察
Onsite 3-4 轮，包括 Coding、System Design、AI Safety 和 BQ。系统设计与 LLM Inference 和 AI 安全高度相关。
- Design an LLM Inference System：大模型推理系统
- Design a Prompt Evaluation System：Prompt 评估系统
- Design a Safety Alignment Framework：安全对齐框架
- Design a Multi-Agent Coordination System：多智能体协同系统
Design LLM Inference System 核心架构：
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import asyncio
class LLMInferenceServer:
def __init__(self, model_path, device='cuda'):
self.model = AutoModelForCausalLM.from_pretrained(model_path)
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.model.to(device)
self.model.eval()
self.device = device
# KV Cache 优化
self.kv_cache_enabled = True
self.max_batch_size = 8
self.max_tokens = 4096
# Request queue
self.request_queue = asyncio.Queue()
self.batch_processor = None
async def generate(self, prompt, max_new_tokens=256,
temperature=0.7, top_p=0.9):
"""
生成响应 - 支持 Streaming
"""
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
use_cache=self.kv_cache_enabled,
return_dict_in_generate=True
)
generated = outputs.sequences[0][inputs.input_ids.shape[1]:]
response = self.tokenizer.decode(generated, skip_special_tokens=True)
return response
async def batch_generate(self, prompts, **kwargs):
"""
批量推理 - 提高吞吐量
"""
batched_inputs = self.tokenizer(
prompts, return_tensors="pt", padding=True
).to(self.device)
with torch.no_grad():
outputs = self.model.generate(
**batched_inputs,
max_new_tokens=kwargs.get('max_new_tokens', 256),
temperature=kwargs.get('temperature', 0.7),
top_p=kwargs.get('top_p', 0.9),
do_sample=True,
use_cache=self.kv_cache_enabled
)
responses = []
for i, seq in enumerate(outputs):
generated = seq[batched_inputs.input_ids.shape[1]:]
resp = self.tokenizer.decode(generated, skip_special_tokens=True)
responses.append(resp)
return responsesAI Safety 核心概念：
- Constitutional AI：基于原则的 AI 对齐方法
- RLHF (Reinforcement Learning from Human Feedback)：人类反馈强化学习
- Red Teaming：AI 系统安全测试
- Interpretability：模型可解释性研究
- Capability Evaluation：能力评估框架
四、Behavioral Questions 高频题库
Anthropic 的 BQ 注重 AI Safety 使命和价值观对齐。高频问题：
- 为什么加入 Anthropic？对 AI Safety 的理解是什么？
- 你如何看待 AI 对齐问题？有什么解决方案？
- 描述一个你在技术决策中考虑安全性的经历
- 如何平衡 AI 能力和安全性？
五、备考策略与核心建议
- AI 领域深度：熟悉 Transformer 架构、Attention 机制、RLHF、Inference 优化
- AI Safety 知识：阅读 Anthropic 的 Research Reports 和 Technical Blog
- 系统设计准备：重点准备 LLM Inference、Prompt Evaluation 系统设计
- 算法基础：LeetCode Medium 保持手感，每天 1-2 题
def softmax(logits, temperature=1.0):
"""Softmax with temperature scaling"""
import numpy as np
scaled = logits / temperature
exp_scaled = np.exp(scaled - np.max(scaled))
return exp_scaled / np.sum(exp_scaled)
# Temperature control:
# T > 1: 更均匀分布 (更有创造性)
# T < 1: 更尖锐分布 (更确定性)class RLHF_Trainer:
"""RLHF 训练流程简化版"""
def __init__(self, model, reward_model):
self.model = model
self.reward_model = reward_model
def train_step(self, prompt, chosen_response, rejected_response):
"""训练一步 - 对比学习"""
# 1. 计算奖励分数
chosen_reward = self.reward_model(prompt, chosen_response)
rejected_reward = self.reward_model(prompt, rejected_response)
# 2. 计算 loss (对比 loss)
reward_diff = chosen_reward - rejected_reward
loss = -torch.nn.functional.logsigmoid(reward_diff)
# 3. 反向传播
loss.backward()
return loss.item()