Applied AI / agent product engineer¶
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AI Engineering · Roles covered: Applied AI engineer, Agent engineer, AI / LLM engineer, Software engineer on AI products
Turns model capabilities into working product behaviour: interfaces, agent runtimes, tools and recoverable failures.
On this page: What the role involves · How it is interviewed (12) · Questions (27) · Preparation
What the role involves¶
This family turns model capabilities into usable workflows. Anthropic's Product Engineer, Computer Use is one concrete variant: it combines interfaces, agent runtime and backend delivery with instrumentation and robustness work. OpenAI's Codex Core Agents posting illustrates a neighbouring infrastructure-heavy variant, including orchestration, sandboxing and persistent state. “Agent engineer” alone does not distinguish these scopes.
Sources: Product Engineer, Computer Use, Anthropic, retrieved 2026-09-29 · Software Engineer, Codex Core Agents, OpenAI, retrieved 2026-09-29
Deliverables and skills: working product behaviour, tool integrations, observable execution and recoverable failures; the mix of full-stack development and distributed systems depends on the variant. For preparation, distinguish adapting an application around a model from changing model weights, and a user-facing workflow from the shared runtime beneath many workflows.
How it is interviewed¶
Stages that sources report for these roles, taken from the company pages. Markers show the basis; interview loops change, so check the dates.
| Company | Stage | What happens | Basis |
|---|---|---|---|
| Anthropic | Product engineer loop Software engineer on AI products |
One candidate in 2025 reports live coding, a sales discussion, a culture interview and a hiring manager interview. | 🗣 candidate report Anthropic Product Engineer Interview Experience - New York, New York, Taro, candidate report, retrieved 2026-09-29 |
| Canva | AI-assisted coding interview Software engineer on AI products, ML engineer / data scientist |
Replaced the computer-science fundamentals screen. A realistic product task worked through with the candidate's own AI tools, which candidates are told about in advance and advised to practise with. The company states it assesses how ambiguous requirements are broken down, whether problems in generated code are found and fixed, and whether the result meets production standards. | ✅ confirmed by the company Yes, You Can Use AI in Our Interviews. In fact, we insist, Canva Engineering Blog, published 2025-06-11, retrieved 2026-09-30 AI Interview Success: An Interviewer's Inside Guide, Canva Engineering Blog, published 2025-10-20, retrieved 2026-09-30 |
| LangChain | Coding assignment in the repository Software engineer on AI products |
Build a real feature on an older branch of the company's repository, with a full specification and test cases. | † prep guide or compilation LangChain Software Engineer Interview Guide, Aced (formerly Exponent), retrieved 2026-09-29 |
| LangChain | System design: critique, then extend Software engineer on AI products |
About thirty minutes evaluating the service architecture for weaknesses, then designing a product feature on the platform. | † prep guide or compilation LangChain Software Engineer Interview Guide, Aced (formerly Exponent), retrieved 2026-09-29 |
| Cursor (Anysphere) | Technical round in the product's code Software engineer on AI products |
Implement a hash tree the product actually uses inside a cloned repository; search, ChatGPT and Cursor were allowed for targeted syntax help, announced to the interviewer before use. | 🗣 candidate report Cursor Software Engineer (New Grad) Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29 |
| Cursor (Anysphere) | Remote onsite build Software engineer on AI products |
Eight to nine hours with access to the codebase: decide what to build, build it, then explain why it was implemented that way and what the trade-offs are. | 🗣 candidate report Cursor Software Engineer (New Grad) Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29 |
| Sierra | 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 |
| Sierra | 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 |
| Sierra | 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 |
| Sierra | 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 |
| Sierra | 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 |
| Sierra | 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 |
Company pages that cover these roles: Anthropic, OpenAI, Amazon, Canva, Databricks, LangChain, NVIDIA, Cursor (Anysphere), Perplexity, Sierra, Tochka.
Questions¶
Reported for these roles¶
Questions whose source says they were asked for one of these roles.
- 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 · Agents, tools and protocols · Asked at: Sierra 🗣
- Sources (Agent engineer): 🗣 Sierra AI Agent Engineer Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29
- Compare DPO with PPO-based RLHF, including the assumptions behind offline preference learning and reasons to collect fresh rollouts.
- Knowledge · Fine-tuning and post-training · ✍ Answer
- Sources (AI / LLM engineer): 🗣 Что теперь нужно знать современному LLM-инженеру, Хабр, рассказ кандидата, published 2026-08-25, retrieved 2026-09-29
- Critique the weaknesses of an existing production service architecture, then design a new product feature on top of it.
- System design · AI system design · Asked at: LangChain †
- Sources (Software engineer on AI products): † LangChain Software Engineer Interview Guide, Aced (formerly Exponent), retrieved 2026-09-29
- 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 · Practical coding · Asked at: Sierra ✅ · ✍ Answer
- Sources (Software engineer on AI products): ✅ The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
- 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 · Practical coding · Asked at: Sierra ✅ · ✍ Answer
- Sources (Software engineer on AI products): ✅ The AI-native interview, Sierra, published 2026-04-22, retrieved 2026-09-29
- Inside a clone of the product's repository, implement a data structure the product actually uses, asking for AI help only on targeted syntax questions.
- Coding · Practical coding · Asked at: Cursor (Anysphere) 🗣 · ✍ Answer
- Sources (Software engineer on AI products): 🗣 Cursor Software Engineer (New Grad) Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29
- Given a real codebase for a working day, decide what to build, ship it, and explain your implementation choices and trade-offs.
- Applied scenario · Practical coding · Asked at: Cursor (Anysphere) 🗣
- Sources (Software engineer on AI products): 🗣 Cursor Software Engineer (New Grad) Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29
- In a spreadsheet where cells can reference other cells, detect whether there is a circular reference.
- Coding · Practical coding · Asked at: Sierra 🗣
- Sources (Agent engineer): 🗣 Sierra AI Agent Engineer Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-29
- In an AI-assisted coding session, explain which models you use and why, how you watch the token budget, and how you give the agent its context and task.
- Applied scenario · Practical coding · ✍ Answer
- Sources (AI / LLM engineer): 🗣 Что теперь нужно знать современному LLM-инженеру, Хабр, рассказ кандидата, published 2026-08-25, retrieved 2026-09-29
Also practise¶
An editorial selection: relevant to the role, but not reported for it.
- Design recovery from tool errors and timeouts, including cases where a timed-out call may already have changed external state.
- System design · Agents, tools and protocols · ✍ Answer
- What does MCP standardise between an AI application and an external service that a model's function-call format does not?
- Knowledge · Agents, tools and protocols · ✍ Answer
- How would you select, name and document tools so that a model chooses the right operation and arguments?
- System design · Agents, tools and protocols · ✍ Answer
- Design memory for an agent that works across many sessions: what is persisted, retrieved, revised and forgotten?
- System design · Agents, tools and protocols · ✍ Answer
- Define completion and stopping conditions for an agent loop so it cannot spend indefinitely on an unfinished task.
- System design · Agents, tools and protocols · ✍ Answer
- Design human approval for consequential agent actions, including how approval remains bound to the exact action being executed.
- System design · Agents, tools and protocols · ✍ Answer
- A long-running agent confidently pursues the wrong objective. How do you locate where its task state diverged and recover?
- Applied scenario · Agents, tools and protocols · ✍ Answer
- Design how a model plans and executes a long-running task through MCP tools while staying reliable within context-window limits.
- System design · Agents, tools and protocols
- What are a language model's generation parameters?
- Knowledge · Agents, tools and protocols
- An agent's context fills up over a long task. How would you decide what stays in the window, what is summarised and what moves out of it?
- System design · Agents, tools and protocols · 🧪 generated from job-posting themes · ✍ Answer
- Modify a prompt so that a target share of the model's outputs matches the desired result, and show how you measured it.
- Applied scenario · Evaluation and observability
- Before launching an assistant that can use tools, how would you organise adversarial testing, and what would a clean result let you claim?
- System design · Safety, security and governance · 🧪 generated from job-posting themes · ✍ Answer
- Design a repository-aware coding assistant, from indexing and context assembly to applying edits and checking results.
- System design · AI system design
- Design a customer-support agent that can execute service actions and transfer the case to a human when needed.
- System design · AI system design · ✍ Answer
- Design natural-language querying over a warehouse with thousands of tables, from schema selection to safe query execution.
- System design · AI system design · ✍ Answer
- Implement a minimal agent runner with validated tool dispatch, error handling and a hard step limit.
- Coding · Practical coding · ✍ Answer
- In an interview where you are expected to use AI tools, how do you stay accountable for code you did not write by hand, and where would you decline what the model produced?
- Applied scenario · Practical coding · ✍ Answer
- Write a prompt that extracts a date range from a customer's request in Russian and tells apart near-identical phrasings such as «с первого числа» (from the first), «по первое число» (through the first) and «за первое число» (for the first).
- Applied scenario · Applied and customer scenarios
Preparation¶
Preparation route: engineering baseline, then agents, retrieval when external knowledge matters, and practical coding. Practise tool retries and human approval. Build a bounded agent beside a simpler fixed workflow; compare task success, latency, cost and failure recovery on the same cases. Defend whether the extra autonomy earns its complexity.
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