AI Interview for Software Engineers — Automate Screening & Hiring

AI Interview Questions for Software Engineers: What to Ask & Expected Answers

When interviewing software engineers — whether manually or with AI Screenr — the right questions separate candidates who can recognise a pattern from candidates who have actually shipped and debugged a production system. Below are the four areas we recommend probing, with the kinds of answers a mid-to-senior engineer will give.

1. Data Structures & Algorithm Thinking

Q: "When would you choose a hash map over a balanced BST?"

Expected answer: "Hash map when I need average O(1) lookups and I don't care about ordering — caches, deduplication, counting, joining datasets by key. Balanced BST (or a sorted structure like a B-tree) when I need ordered traversal, range queries, or predecessor/successor lookups — for instance, a leaderboard where I need top-N by score, or indexing time-series data for range scans. Hash maps also fall apart under adversarial input if the hash function is weak, and they give amortised not worst-case guarantees, so in latency-sensitive paths I think about whether a 99th-percentile rehash stall is acceptable. For small N — say under a hundred keys — a plain array with linear scan usually beats both on cache behaviour."

Red flag: Candidate says "hash map is always faster" or can't name a case — range queries, ordered iteration — where a BST wins.

Q: "Walk through how you'd approach a problem you'd never seen before."

Expected answer: "Clarify before coding. I restate the problem in my own words, ask about input size and edge cases — empty input, duplicates, overflow, negative numbers — and confirm the invariants. Then I work a small concrete example by hand; patterns usually emerge from the example, not from staring at the abstract problem. I state a brute-force solution out loud so we agree on correctness first, then look for structure to optimise — sorted input suggests binary search, repeated subproblems suggest DP or memoisation, graph structure suggests BFS/DFS. I talk through Big-O as I go and name the trade-off I'm making rather than jumping to the 'clever' solution. If I get stuck I invert the problem or reduce it to a simpler version."

Red flag: Candidate jumps to coding before clarifying requirements or edge cases, and never says a Big-O out loud.

Q: "When is it worth rolling your own data structure versus using the standard library?"

Expected answer: "Almost never roll your own first — the standard library version is battle-tested, has known complexity guarantees, and other engineers recognise it. The exceptions I've actually hit: a hot loop where profiling showed the generic container's cache behaviour was costing us (flat array of structs beat a hash map of pointers by 3x), a custom bloom filter when memory was constrained and false positives were acceptable, and a concurrent queue where the standard options didn't support the exact consumer pattern we needed. In every case I benchmarked the standard library version first, confirmed it was the bottleneck, and wrote a targeted replacement with property tests. Rolling your own without measurement is how you ship a subtle bug that takes a year to surface."

Red flag: Candidate says "I'd write my own for performance" with no profiling evidence that the standard library is actually the bottleneck.

2. System Design Fundamentals

Q: "Design a URL shortener — walk through the components."

Expected answer: "Start with scale assumptions — say 100M URLs, 10:1 read-to-write ratio, sub-100ms reads. Core components: an API layer, an ID generator, a primary store, and a cache. For IDs I'd use base62-encoded counters from a sharded sequence or Snowflake-style IDs — not hashes, because collisions at scale are a real problem. Primary store is Postgres keyed on the short ID with the long URL and metadata; I'd front it with Redis for hot reads since the traffic is skewed power-law. For analytics I'd write events to a queue — Kafka or Kinesis — and aggregate async rather than updating click counts on the read path. Failure modes I'd address: cache stampede (single-flight or stale-while-revalidate), abuse (rate limiting at the API edge), and expired links (soft-delete with a TTL on the cache)."

Red flag: Candidate draws boxes without stating scale assumptions, picks MD5 hashes for IDs, or never mentions a single failure mode.

Q: "How do you think about read-heavy vs write-heavy workloads?"

Expected answer: "Read-heavy workloads are cache-friendly — the leverage is in avoiding the database entirely for most requests. I'd use Redis or an in-process cache, think about cache invalidation early (TTL plus event-based bust on write), and add read replicas once a single primary can't handle the residual traffic. Write-heavy workloads invert the problem — you can't cache writes, so the bottleneck is primary throughput. Options are batching, async queues decoupling acknowledgement from durability, partitioning/sharding by a sensible key, and picking a storage engine whose write path matches your pattern — LSM-based stores like Cassandra or RocksDB eat sequential writes more cheaply than B-tree-based stores. The hardest case is mixed — a hot key that's written and read concurrently — where you usually end up with a single-writer-per-key design and aggressive caching of the read side."

Red flag: Candidate reaches for "just add Redis" on every problem without distinguishing read-side caching from write-side throughput constraints.

Q: "How do you reason about consistency trade-offs in a distributed system?"

Expected answer: "Start with what the product actually needs, not with CAP as an abstraction. For most user-facing features, read-your-writes and monotonic reads are the guarantees that matter — a user who updates their profile expects to see the update on refresh, even if another user sees it a second later. I default to strong consistency for anything involving money or auth, eventual consistency for analytics and feeds. Implementation-wise that usually means a primary-with-replicas Postgres for the strong-consistency slice and something like DynamoDB or Cassandra for the eventual-consistency slice. I also think about the failure modes — split-brain, stale reads, duplicate writes — and make them explicit with idempotency keys, version numbers, or conflict-resolution policies rather than hoping they don't happen. The expensive mistake is using eventually consistent storage for something that needed strong consistency and discovering it in an incident."

Red flag: Candidate recites "CAP theorem, pick two" without mapping guarantees to actual product requirements like money flows or read-your-writes.

3. Code Quality, Reviews & Testing

Q: "What makes a good code review comment?"

Expected answer: "Specific, actionable, and hierarchical. I separate nits from blockers explicitly — a prefix like 'nit:' or 'question:' versus plain criticism — so the author knows what has to change before merge versus what's a suggestion. I prefer questions over assertions for anything non-obvious: 'what happens if this list is empty?' surfaces intent and catches me when I'm wrong. I review architecture before style — code-formatting feedback on a PR that has the wrong abstraction is noise. I try to approve with comments rather than block when I can, so small iterative improvements don't get stuck. And I praise good decisions out loud; review culture goes sideways fast when the only feedback is criticism."

Red flag: Candidate describes code review as nit-picking style issues only, with no sense of architectural push-back or nit-vs-blocker separation.

Q: "How do you decide what to test and at what layer?"

Expected answer: "I follow the testing trophy rather than the classic pyramid — more integration, fewer narrow unit tests. Unit tests for pure functions with real branching logic, mathematical code, or domain rules that change frequently. Integration tests for anything involving more than one module talking to a real database or HTTP layer — I use something like Testcontainers so I'm hitting real Postgres rather than mocks. End-to-end only for the handful of critical user journeys that would cost us the business if broken. I avoid mocks for anything I own; mocks test that I called the interface, not that the system works. Coverage number is a smoke check — I care more about whether a sensible refactor breaks tests for the right reasons."

Red flag: Candidate mocks every dependency and chases line-coverage numbers without asking whether the tests would actually catch a real regression.

4. Production Debugging & Incident Response

Q: "A service is returning 500s intermittently — walk me through how you'd debug."

Expected answer: "First triage: is it a new behaviour? I look at deployment timing and correlate with the error rate graph in Datadog or whatever observability stack we use — if errors started at a deploy, that's my first lead. Then I scope the blast radius: all users or a subset, all endpoints or one, all regions or one. Error messages go into the grouping tool; I look for a common stack frame. If distributed, I pull the OpenTelemetry traces for failing requests and look for the span that's actually throwing — it's often downstream of where the 500 surfaces. Common causes ranked by how often they've actually been the answer for me: downstream dependency degraded, connection pool exhausted, recent migration with a slow query, memory leak causing GC pauses. Once I have a hypothesis I reproduce in staging before rolling back blindly — rollbacks that aren't based on evidence sometimes break the thing that was holding the system together."

Red flag: Candidate says "I'd just roll back the deploy" without scoping blast radius, correlating to recent changes, or forming a hypothesis first.

Q: "Tell me about a production incident you handled."

Expected answer: "The most useful one: our job-processing queue backed up overnight because a deploy introduced a slow query that was fine under light load but collapsed under the morning batch. I was on-call. First thing I did was stop the bleeding — paused the batch, which stopped the backlog growing. Then I ran EXPLAIN ANALYZE on the suspected query, confirmed the plan had regressed to a sequential scan because the new code path passed a nullable parameter that killed index use. Short-term fix: reverted the specific handler rather than the whole deploy, so we didn't lose other changes. Drained the backlog with a rate-limited replay. The postmortem identified two systemic issues — no load test on the batch path, and no alert on queue depth trend versus threshold — both of which became follow-up work. The durable lesson for me was that query-plan regressions are silent until load hits, so I now EXPLAIN any new query against production-like data before it ships."

Red flag: Candidate describes an incident as "it broke, we fixed it" with no bisection, no stop-the-bleeding step, and no systemic follow-up from the postmortem.

Q: "How do you use observability tooling to debug something you can't reproduce locally?"

Expected answer: "The three pillars matter for different questions. Metrics tell me something is wrong — an elevated p99, a rising error rate, a queue depth trending upward. Logs tell me what specific requests looked like when it went wrong — I grep by trace ID or correlation ID, never by timestamp alone. Distributed traces tell me where in the request path the wrong thing happened — which span is slow, which downstream call failed, which retry storm is amplifying the problem. My default when I can't reproduce: pull a handful of failing trace IDs from the error-rate spike, open each trace in Datadog or Grafana Tempo, and look for the common pattern — same downstream service, same query shape, same user cohort. If observability doesn't cover the question I'm asking, that's a follow-up item — add the span or metric that would have answered it, because the same class of bug will come back."

Red flag: Candidate only mentions logs and greps by timestamp, with no awareness of trace IDs, distributed tracing, or closing observability gaps afterward.