10 Character AI Software Engineer (New Grad) Interview Questions (2026)

Outline

The company builds consumer-scale AI characters and entertainment products. Tie your answer to something concrete — a specific product you've used, an interest in interactive media, or the scale challenges of running inference for millions of users. Avoid generic praise of large language models; talk about the product side.

Outline

STAR. Concrete user-facing project — a feature, a tool, a side project. Cover what users told you (qualitative) AND any numbers you have (DAU, retention, NPS). End with what you'd do differently. Consumer AI engineers need product instincts; show yours.

Outline

STAR. Pick a concrete technical disagreement (architecture, library choice, scope). Show how you presented data, listened to the other side, and either won, lost, or compromised gracefully. End with what you learned. Avoid villainizing the teammate.

Outline

Recursive: if root is null or matches either target, return root. Recurse left and right. If both sides return non-null, root is the LCA; else return whichever side is non-null. O(n) time, O(h) stack. Follow-up: with parent pointers? — convert to a 'find intersection of two linked lists' problem.

Outline

Trick: s2 is a rotation of s1 iff s2 is a substring of s1+s1. Implement substring check directly or via KMP. O(n) time, O(n) space. Follow-up: do it without the concatenation trick — two-pointer alignment for each possible rotation, O(n^2) worst case.

Outline

Token-bucket or sliding-window counter per user. Hash map of user_id to deque of timestamps within the window. On each message: evict expired entries, count remaining, accept or reject. O(1) amortized. Discuss memory (entries-per-user * users) and the cleanup strategy. Mention how this scales when you shard by user_id.

Outline

Walk the transcript from the end. Maintain a running token count using a provided tokenizer (or len-based stub). Stop when adding the next turn would exceed the budget. Return the suffix. O(n) worst case. Discuss edge cases: a single turn already over budget (truncate it), boundary on partial turns.

Outline

Layered: confirm the alert (monitoring sanity check), correlate with deploys, GPU/CPU saturation, queue depth, batch-size shifts, model server health, network. Mention quick mitigations: rollback, capacity scale-out, batch-size cap. Show a structured methodology over a hero-fix instinct.

Outline

Naive: score each conversation (substring count, BM25, or embedding similarity), keep a min-heap of size K. O(n log K). Discuss the tradeoff: lexical (fast, brittle) vs semantic (slower, robust). Mention precomputing embeddings if conversations don't change often.

Outline

Key by (user_id, normalized_prompt_hash) into a tiered cache: in-memory LRU per pod, then a shared store (Redis-like), then origin. Discuss TTLs, invalidation when context shifts, and the cost-vs-quality tradeoff of caching generative outputs. Mention safety: cached harmful outputs shouldn't get pinned.