Anthropic Coding & Algorithms interview questions tend to skew practical and systems-minded rather than purely contrived puzzles. Expect live, timed coding rounds (often in a shared Python environment or CodeSignal), system-design-style problems for scaling and reliability, and behavioral questions tied to safety and collaboration. Interviewers evaluate problem decomposition, correctness, edge-case handling, performance trade-offs, code clarity and modularity, and how you reason about concurrency, data mutation, and robustness under changing requirements. Deep follow-ups are common: a working solution is a starting point, not the finish line. For interview preparation focus on building polished, testable implementations of multi-stage problems (for example in-memory stores, streaming/aggregation tasks, or concurrent data structures), practicing clear verbal articulation of trade-offs, and rehearsing concise safety- and values-aligned stories. Time-boxed mock interviews in the same tools you’ll use, steady practice with Python standard library idioms, and walking through post-solution optimizations will pay off. Emphasize readability, small iterative steps, and how you validate and harden code — those habits map directly to what Anthropic seeks.