Sierra forward deployed engineer interview

Process

1. Recruiter screen

   ~30-min call. Assesses: Background, motivation and why Sierra, preferred language (Python/TypeScript), level and pay expectations. [3]

2. System design phone screen (current)

   Replaces the former coding phone screen; ~60 min per third-party guide. Assesses: Design judgment for production systems. [1]

3. Legacy technical/coding screen (older loop)

   60-min live coding, LLMs not allowed per one candidate. Assesses: Data structures (graph/tree traversal), clean reasoning. [3]

4. Legacy take-home (older loop)

   Build a customer-support agent for a fictional client before the onsite; reported as paid in one listing. Assesses: Product judgment, prioritization, agent fundamentals, production thinking. [3]

5. AI-native onsite: Plan

   Working session; candidate drives ideation for a product in their own domain while interviewers probe. Assesses: Product thinking, scoping, initiative. [1]

6. AI-native onsite: Build

   ~2 hours solo; interviewer leaves; any AI tools and frameworks allowed; scope may change. Assesses: Agency, scoping under time pressure, technical judgment (data model, abstractions, extensibility). [1]

7. AI-native onsite: Review

   Demo plus discussion of product flows, code review, path to production, and how AI was used. Assesses: Judgment, system understanding, production readiness, AI usage. [1]

8. Debugging interview

   Pilot: review and improve a colleague's draft PR in a mid-sized codebase using coding agents. Older version: 60 min fixing bugs in a TypeScript/React app. Assesses: Code reading, systematic diagnosis, customer impact of bugs. [1]

9. Agent presentation (older loop)

   Walk through take-home agent, then present a technical topic to a non-technical audience. Assesses: Design defense, communication, customer orientation. [3]

10. Hiring manager / behavioral

    Resume and past-experience conversation. Assesses: Collaboration, ownership, customer interaction, values alignment. [3]

11. Onsite logistics

    Interviews held in person at a Sierra office. Assesses: Values alignment as well as skills. [2]

AI tools in interviews: Onsite build: candidates may use AI tooling and frameworks of their choice, and interviewers discuss how AI was used. AI policy for the piloted debugging round was 'still TBD' at publication. A 2025 candidate reported LLMs were not allowed in the older live technical interview (syntax lookup was). [1]

Questions

System design 2

• What metrics and observability would you add to your agent once it runs in production?

  System designThird-party guideAgent Engineer2025[3]

• Design an agentic service for a use case such as a subscription-cancellation flow.

  System designThird-party guideAgent Engineer2025[3]

Coding 3

• Detect circular references between spreadsheet cells that reference other cells.

  CodingThird-party guideAgent Engineer2025[3]

  Also listed on S5; S3 says reported by candidates

• Python code throws intermittently; add exponential-backoff retries so it keeps trying until it succeeds.

  CodingCandidate reportAPX (early-career, customer-facing)2025[4]

• Given a nested merchandise catalog, find every item related to item A, then items related to those children.

  CodingCandidate reportAPX (early-career, customer-facing)2025[4]

  Candidate felt BFS/DFS would have been stronger than nested loops

Take-home 1

• Build a customer-support AI agent for a fictional client; which two features would you prioritize and why?

  Take-homeThird-party guideAgent Engineer2025[3]

AI build 1

• Plan a product in your own domain with interviewers, then build it alone in about two hours using any AI tools.

  AI buildEmployer-publishedSoftware Engineer, Agent2026[1]

  Sierra's examples: an AI game meant to keep the player in flow; a headless simulation tool driven by an agent

Debugging 3

• Review a teammate's draft PR adding a cross-cutting feature to a medium-sized codebase, then improve it with coding agents.

  DebuggingEmployer-publishedSoftware Engineer, Agent2026[1]

  Described as a pilot; AI policy for this round was still undecided

• Find and fix the bugs in a prewritten TypeScript/React app and explain how each one affects customers.

  DebuggingThird-party guideAgent Engineer2025[3]

• Using a user-journey flowchart, CLI output, and code, find the bugs and explain how each hurts the customer.

  DebuggingCandidate reportAPX (early-career, customer-facing)2025[4]

  Candidate found about four bugs before time ran out

Customer scenario 1

• Design an AI agent for a streaming service, going beyond obvious support and recommendation use cases.

  Customer scenarioCandidate reportAPX / Agent Engineer2025[4]

  Interviewer pushed back that Sierra already does the conventional ideas; also listed on S5

Presentation 2

• Demo what you built; defend product flows, walk through the code, describe the path to production and how you used AI.

  PresentationEmployer-publishedSoftware Engineer, Agent2026[1]

• Present a technical topic of your choosing to a non-technical audience.

  PresentationThird-party guideAgent Engineer2025[3]

Domain 2

• Explain how retrieval-augmented generation (RAG) works.

  DomainThird-party guideAgent Engineer2026[5]

  Page does not mark individual questions as candidate-reported

• Describe your approach to prompt engineering.

  DomainThird-party guideAgent Engineer2026[5]

Behavioral 2

• Explain a technical concept to a non-technical person.

  BehavioralThird-party guideAgent Engineer2026[5]

• Tell me about handling conflicting stakeholder priorities and getting alignment.

  BehavioralThird-party guideAgent Engineer2026[5]

Motivation 2

• How do you use AI yourself, and where does AI apply in customer support?

  MotivationCandidate reportAPX (early-career, customer-facing)2025[4]

• Why do you want to work at Sierra, and how do you keep up with AI news?

  MotivationCandidate reportAPX / Agent Engineer2025[4]

Tips from the sources

• Sierra says candidates get evaluation criteria and advice in advance; cutting scope during the build is acceptable. [1]
• Skip boilerplate such as CRUD screens and auth; spend the build time on what makes the idea distinctive. [1]
• Interviewers watch for agency, such as changing course when stuck, and will ask how you used AI. [1]
• A candidate advised less classic LeetCode and more practice reading unfamiliar Python fast, tracing bugs through logs or flowcharts, and stating customer impact. [4]
• For product-design prompts, the same candidate said to find places AI is not yet used rather than repeating obvious ideas, and to study Sierra's product. [4]
• Third-party guide: build a small agent without a framework and practice tool calling; tie each technical decision to customer impact. [3]
• Candidate reports describe several generations of the loop; confirm round type, length, and AI rules from your own invitation. [6]

Not found in public sources: No first-hand candidate write-up of the new AI-native onsite yet (all candidate reports predate it). No public detail on the Strategist, Agent Development or Deployed Infrastructure Engineer loops. Glassdoor/Blind reports were found but excluded per policy.

Sources

1. The AI-native interview. Sierra (Vijay Iyengar, Arya Asemanfar, Angie Wang), 2026-04. Employer-published
2. Careers. Sierra, 2026. Employer-published
3. Sierra Agent Engineer interview guide (formerly tryexponent.com). Aced (formerly Exponent), 2026. Third-party guide
4. Sierra AI interview experience (APX, L3). Aced (candidate submission), 2025. Candidate report
5. Sierra Agent Engineer interview questions. Aced (formerly Exponent), 2026. Third-party guide
6. Sierra AI-Native Software Engineer Interview Guide 2026. PracHub, 2026-09. Third-party guide
7. Sierra FDE jobs page. Aced, 2026-09. Third-party guide