Sierra

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AI-native products · International · Loop reviewed 2026-09-29

In April 2026 Sierra rebuilt its engineering interviews around coding agents: a system design screen, then an onsite where the candidate plans, builds and defends a product in about two hours with the AI tools of their choice. Earlier loops, reported by a candidate, used a take-home agent build.

On this page: Interview loop · Official preparation material · Questions (4)

Roles covered

• AI Engineering: Agent engineer, Software engineer on AI products

Interview loop

Stage	What happens	Basis
System design screen Software engineer on AI products	Replaced the earlier coding screen, in which candidates wrote code without AI: the company considers taking an application into production at scale the harder problem.	✅ confirmed by the company The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
Onsite: plan Software engineer on AI products	A working session in which the candidate drives the definition of a product in their own domain while interviewers question it.	✅ confirmed by the company The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
Onsite: build Software engineer on AI products	The interviewer leaves and the candidate builds the product for about two hours with the AI tools and frameworks of their choice, free to cut or change scope.	✅ confirmed by the company The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
Onsite: review Software engineer on AI products	A demo and a debate of the key product flows; a code review of the data model, abstractions and extensibility; the path to production; and how AI was used.	✅ confirmed by the company The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
Debugging interview (pilot) Software engineer on AI products	A medium-sized codebase and a colleague's draft pull request that adds a cross-cutting feature: review and improve it, iterating with coding agents. How much AI is allowed was still undecided.	✅ confirmed by the company The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
Earlier take-home and onsite Agent engineer	One candidate, about a year before retrieval: build a customer-support agent for a fictional company, choosing two of five features and explaining how to measure it in production; then a TypeScript and React debugging round and a defence of the design.	🗣 candidate report Sierra AI Agent Engineer Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29

Coding: required. The build round is hands-on programming with AI tools allowed. (The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29)

Official preparation material

• The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29

Questions reported for Sierra

Agents, tools and protocols

• Build a customer-support agent for a fictional company: choose two of five proposed features, justify the choice and explain how you would measure the agent in production.
  • Applied scenario · Asked at: Sierra 🗣
  • Tests: Whether you prioritise scope under a deadline and plan measurement for a working agent.
  • A strong answer covers:
    • Choose the two features by ticket volume, verifiable resolution and the risk of acting wrongly.
    • State what you cut and why, and keep escalation to a human working end to end.
    • Measure genuine resolution, repeat contacts, escalation quality and harmful actions, not conversations closed.
  • Read: Demystifying evals for AI agents (Anthropic)

Practical coding

• Plan a product with the interviewer, then build it in about two hours with the AI coding tools of your choice and defend its design, code and path to production.
  • Applied scenario · Asked at: Sierra ✅ · ✍ Answer
  • Tests: Whether you scope, build and defend a working product with AI assistance under a time limit.
  • A strong answer covers:
    • Agree the user flow, smallest working slice, time budget and deliberate omissions before delegating bounded pieces.
    • Give each AI task interfaces, relevant context and acceptance checks; inspect diffs and demonstrate behaviour instead of counting generated code.
    • Defend architecture, algorithm assumptions and rejected suggestions; distinguish verified behaviour from production gaps and an unfinished prototype.
  • Read: The AI-native interview (Sierra) · AI Interview Success: An Interviewer's Inside Guide (Canva Engineering Blog)
• Review and improve a colleague's draft pull request that adds a cross-cutting feature to an unfamiliar codebase, working alongside coding agents.
  • Applied scenario · Asked at: Sierra ✅ · ✍ Answer
  • Tests: Whether you judge someone else's change critically instead of accepting what an agent proposes.
  • A strong answer covers:
    • Trace the intended behaviour, entry points, callers and existing invariants before asking the agent to explain or change the diff.
    • Check every affected path, especially authorisation, retries, shared state and migrations; construct a counterexample and a focused regression check.
    • Separate observed defects from hypotheses, verify agent suggestions and explain the approval decision and residual risks; respect the session's AI limits.
  • Read: The AI-native interview (Sierra) · AI Interview Success: An Interviewer's Inside Guide (Canva Engineering Blog) · Demystifying evals for AI agents (Anthropic)
• In a spreadsheet where cells can reference other cells, detect whether there is a circular reference.
  • Coding · Asked at: Sierra 🗣
  • Tests: Whether you model dependencies as a graph and detect cycles correctly and efficiently.
  • A strong answer covers:
    • Clarify reference forms, ranges and self-references, and whether to report any cycle or every cell in one.
    • Build a dependency graph and run three-colour DFS or Kahn's algorithm in O(cells + references).
    • Test self-loops, long chains against recursion depth, disconnected parts, and rechecking after one formula changes.
  • Read: Algorithms, fourth edition: Directed Graphs (Robert Sedgewick and Kevin Wayne, Princeton University)

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