I am studying for a PhD in Computer Science (AI/ML) at UCL. When I was applying for a job last year, I noticed that Anthropic was expanding its business in North America on a large scale, and Canada was also a key focus, so I decisively seized the opportunity and applied for a Research Engineer position in the Canadian office. Finally successfully entered the final page, where the entire Anthropic The AI Research Engineer interview process, question types and some key insights are all reviewed to serve as a reference for students who also want to enter the top AI lab.
Anthropic AI Research Engineer Timeline (Real Rhythm Reference)
Anthropic’s recruitment rhythm is relatively flexible, but the core hiring season is basically concentrated in Q1 (January–April).
My process takes about 6–8 weeks:
- 2025/12/18: Submit application
- 2026/1/22: Preliminary screening surface (40 min)
- 2026/2/4: OA (90 min, CodeSignal)
- 2026/2/10: VO (4 rounds × 1h)
- 2026/2/19: HR follow-up + extra noodles
Initial screening surface (30–40 min)
The core depends on three things:
- Do you understand AI security?
- Have you ever actually done anything related to this?
- Are you a "person with the right values"?
High-frequency issues (basically all around these)
- What is RLHF? What are the practical issues?
- What is Constitutional AI? What problem is solved?
- What do you think is the biggest AI security risk right now?
- How to evaluate whether a model is "honest"?
- Introduce your most representative project (will definitely dig deeper)
- What do you do if a model exhibits unsafe behavior?
OA (90 minutes)
Platform: CodeSignal
Question type: 1 coding + 2 theory
VO (4 rounds)
First round of Coding
Implement a ResponseSafetyFilter:
To support:
- Multi-rule detection (bias/harmful/privacy/hallucination)
- 0–1 safety score
- Strictness control
- Cache + explainability
Problem-solving ideas
- 架构设计：采用 Strategy Pattern + Composite Pattern。每个安全规则（bias / harmful / privacy / hallucination）实现一个独立的 SafetyRule 接口或抽象类，每个规则返回 0–1 的分数。
- Scoring mechanism：总分数 = 加权平均或分层打分（例如 harmful 权重更高）。引入 strictness 参数（0.0–1.0），作为阈值或缩放因子（strictness 高时更严格）。
- Cache：使用 LRU Cache（或 Redis 如果是分布式）缓存 prompt + response 的安全结果，key 可以是 prompt_hash + model_version。
- Explainability：每个规则返回一个 Explanation 对象（包含 rule_name、score、reason、evidence）。最终输出带详细解释的 JSON 或对象，便于审计。
- 实现建议：用 Python 时推荐 dataclasses + functools.lru_cache；Java 时用 ConcurrentHashMap + 策略模式。
Second round of Coding
Design a simplified version of the Constitutional AI pipeline:
- Principle definition
- Data generation
- Model training
- Evaluation
Problem-solving ideas:
- Principle definition：用一个列表或配置文件存储宪法原则（e.g. “Choose the response that is more helpful and harmless”）。支持多条原则随机抽样或优先级排序。
- 数据生成（Critique & Revision）:
- 用基础模型生成初始 response。
- 让模型（或另一个 critic model）根据随机抽取的宪法原则进行 self-critique。
- 生成 revised response。
- Model training:
- Supervised Fine-Tuning（SFT）：用 revised responses 作为标签 fine-tune 基础模型。
- Reinforcement Learning from AI Feedback（RLAIF）：生成 response pairs，让 critic model 根据宪法原则打分，训练 reward model，再用 PPO 或 DPO 优化。
- Evaluation：实现 harmlessness score、helpfulness score、宪法遵守率（constitution compliance rate）、human preference 模拟评估。
System Design
Request support:
- Multi-model parallel training
- Terabytes of data + human feedback
- Real-time security monitoring
- A/B testing
- Explainable & Auditable
Problem-solving ideas:
- 数据层：TB 级人类反馈 + 生成数据 → 使用数据湖（S3 + Iceberg / Delta Lake） + 特征存储。支持版本控制（DVC 或 LakeFS）。
- 训练层：多模型并行训练 → 支持数据并行、模型并行、ZeRO 优化。使用 Ray / Kubernetes + DeepSpeed / Megatron。
- Real-time security monitoring：在线 inference 时接 ResponseSafetyFilter，实时计算多维度分数，超过阈值触发警报或 fallback。使用 Prometheus + Grafana 监控。
- A/B Testing：模型版本 + 流量路由（e.g. 10% 流量给新安全模型），收集用户反馈和安全指标，自动或手动决策。
- Explainable & Auditable：每个 response 记录 constitution principles used、critique trace、safety scores。所有日志存入审计数据库（支持查询和导出）。
- Trade-offs：成本 vs 延迟、准确率 vs 覆盖率、实时性 vs 批量处理。
Culture + Leadership
This round is actually the round with the most invisible eliminations.
Core look:
- Do you genuinely care about AI safety?
- Are you a team player
- Do you have the ability to think long term?
Explicit Red Flags:
- Just want to make money
- Disagree with AI safety
- Lone wolf personality
- Downplaying risk issues
It is recommended to use STAR to answer all questions and talk more about trade-off.
Interview results & experience sharing
In March 2026, I finally succeeded in getting an offer from Anthropic Research Engineer. Fortunately, at the critical stage I had the help of Programhelp Professional interview assistance, including high-frequency real-question review, mock interview guidance, and real-time assistance, ultimately helped me pass all links steadily.
If you are also sprinting for top AI security/alignment positions such as Anthropic, OpenAI, DeepMind, etc., it is highly recommended that you prepare the system in advance.
