anthropic-technical-deep-dive

Anthropic Technical Deep Dive

Build genuine, defensible technical opinions on Anthropic's core research areas. This is the competitive edge for demonstrating real intellectual engagement with Anthropic's mission -- not reciting papers, but showing you have thought critically about the work and can connect it to your own engineering experience.

When to Use

Use for:

• Preparing for Anthropic-specific technical interview rounds
• Developing nuanced opinions on Constitutional AI, RLHF, interpretability
• Bridging a CV/ML/engineering background to alignment and safety work
• Practicing articulation of complex AI safety trade-offs
• Understanding Anthropic's product landscape and strategic position
• Preparing for "what do you think about X?" style questions

NOT for:

• General ML system design interviews (use ml-system-design-interview)
• Behavioral/values interview prep (use values-behavioral-interview)
• Coding interview prep or algorithm practice
• Writing research papers or conducting original research
• Preparing for interviews at other AI labs (different emphasis areas)

Topic Landscape

mindmap
  root((Anthropic Technical Interview))
    Constitutional AI
      Principle-based alignment
      RLAIF vs RLHF
      Principle conflicts
      Scalable oversight
    RLHF & Training
      Reward modeling
      PPO and alternatives
      Alignment tax
      Reward hacking
      DPO / Direct alignment
    Interpretability
      Circuits and features
      Superposition
      Sparse Autoencoders
      Scaling Monosemanticity
      Golden Gate Bridge Claude
      Mechanistic interpretability
    Scaling Laws
      Kaplan et al
      Chinchilla optimal
      Data-constrained scaling
      Emergent capabilities
      Predictability vs surprise
    Context Engineering
      Long context retrieval
      MCP protocol
      Tool use architecture
      RAG vs long context
      Prompt engineering at scale
    Agentic Systems
      Computer use
      Claude Code
      Agent evaluation
      Trust and safety
      Tool use reliability
      Multi-agent coordination
    AI Safety
      Alignment tax
      Deceptive alignment
      Sandbagging
      Responsible Scaling Policy
      Frontier risk assessment
      Red teaming
    Anthropic Culture
      Race to the top
      Responsible development
      Commercial safety tension
      Interpretability as priority