# The Google Cloud FDE interview: the coding round, the agent design round and the Customer Engineer trap

> A close reading of the job posting tells you what most rounds will ask. The exception is the algorithmic coding round, which needs a different kind of practice.

Original: https://fdetimes.net/en/guides/google-cloud-fde-interview-guide/

Google Cloud's job posting for a Forward Deployed Engineer, Applied AI contains one line that all but announces the design round: build high-performance eval pipelines and observability frameworks for complex agentic workloads. No line in the posting announces the coding round.

Aced (formerly Exponent) describes the coding round as a standard data structures and algorithms problem, solved within a time limit. FDE Academy, in a separate piece on interview questions, describes it the same way.

So you need two kinds of preparation: read the job posting for the design and behavioural rounds, and practise algorithms the old-fashioned way for the coding round.

Nor should you treat the order of rounds as fixed. Scaler Topics stresses that this is only a pattern reported by candidates, not an official process. Aced suggests the total number of rounds is still roughly that of a standard Google onsite, but advises checking with your recruiter because the process is new.

Beyond the rounds themselves there is a trap few people mention. If you answer like a Customer Engineer, talking about demos and closing deals, you are signalling that you fit a different job.

All the practice below fits into one evening. You need Python 3, an editor, a timer and the job posting open on screen. Every code snippet here is a cut-down practice version, not a real interview question.

## Step 1: read the job posting like an exam paper

Each line of the posting hints at a question. The line about eval pipelines and observability is what the design round will dig into. The preferred-experience section mentions multi-agent systems built with frameworks such as ReAct and self-reflection.

Two other lines belong to the behavioural and customer round, not the algorithm round: experience troubleshooting live systems, and willingness to travel up to 50% of the time. Prepare honest answers to both, especially the second, because it is a lifestyle condition, not a skill.

If you have never built a multi-agent system, do not rule yourself out. ReAct and self-reflection sit under preferred qualifications, not requirements, and the exercise in Step 3 gives you a starting point.

First task: highlight every requirement, then write next to each one a true story from your own work. Any line without a story is where you need more practice.

## Step 2: the coding problem is small, but expect follow-ups

Accounts of the coding round differ. Aced and FDE Academy call it a standard data structures and algorithms problem, while Scaler Topics says it leans towards data processing and API integration, with code clean enough for someone else to take over.

The safe approach is to practise both kinds. One candidate wrote on Blind that once the requirements were clarified, the coding itself was fairly simple: a small problem about converting identifiers.

Imagine the task is converting variable names from camelCase to snake_case. Start a timer and talk through each decision as you go, including edge cases such as acronyms.

```python
def camel_to_snake(name: str) -> str:
    out = []
    for i, ch in enumerate(name):
        if ch.isupper() and i > 0:
            prev_lower = name[i - 1].islower()
            next_lower = i + 1 < len(name) and name[i + 1].islower()
            if prev_lower or next_lower:
                out.append("_")
        out.append(ch.lower())
    return "".join(out)

assert camel_to_snake("userId") == "user_id"
assert camel_to_snake("HTTPServer") == "http_server"
assert camel_to_snake("getHTTPResponse") == "get_http_response"
```

Check: all three asserts must pass without error. If `HTTPServer` comes out as `h_t_t_p_server`, you forgot to handle acronyms. Next time, raise that case before you start writing code.

The same candidate said that after the coding problem, the conversation moved to a repo-wide migration scenario: finding every place that needs renaming in a large codebase.

That is one person's experience, not a general pattern. But preparing for it costs little, and open-ended questions like this make it easy either to drown in detail or to answer too thinly.

So prepare two versions in advance. The one-minute version has four points: list every place that needs changing, use an automated code-modification tool, let tests catch the errors, and roll out in stages.

The five-minute version adds rollback and the places where code refers to names through strings. Start with the short version, then ask the interviewer which part they want you to go deeper on.

## Step 3: the design round asks about failure too

Aced describes the system design round as testing how you design intelligent systems that combine machine learning with agentic components at scale. FDE Academy says it assesses how you reason about orchestration, tool calling and error handling in a multi-step agent workflow.

The Blind candidate said this round was mostly a discussion of building and scaling agent systems. Handling an agent that gets stuck or falls into an infinite loop was just one of the topics, alongside operational trade-offs.

Imagine a customer needs a customer-service agent that can look up orders and process refunds. Draw the happy path first, then show the interviewer that the agent stops itself at the right moment when something goes wrong. Below is a cut-down ReAct-style loop with a step cap and repeat detection.

```python
def run_agent(plan_step, act, max_steps=8):
    seen, trace = set(), []
    for step in range(max_steps):
        action = plan_step(trace)      # the model picks the next step
        if action["tool"] == "finish":
            return {"status": "done", "trace": trace}
        key = (action["tool"], repr(action["args"]))
        if key in seen:                 # exact repeat of a tool call
            return {"status": "loop", "trace": trace}
        seen.add(key)
        trace.append((action, act(action)))
    return {"status": "max_steps", "trace": trace}
```

Check: write a fake `plan_step` that returns the same tool call every time. `run_agent` must return `loop` on the second step rather than running until the budget is exhausted.

FDE Academy advises volunteering how you would monitor, trace and debug the system in production. DoiT, writing about FDEs in general, argues that observability tooling matters as much as the infrastructure itself.

The three states `done`, `loop` and `max_steps` are where to start: count the rate of each state across a sample set of conversations, store the `trace` to find root causes, and hand off to a human when the agent hits `loop`.

If time allows, mention self-reflection, the technique the posting lists under preferred qualifications. A simple version is to have the agent reread its own trace before retrying.

## Step 4: "how is this different from a Customer Engineer?"

The two titles are easily confused. FDE Academy draws a clear line: a Customer Engineer supports the sale of Google Cloud, while an FDE builds and owns production systems inside the customer's environment. DoiT likewise defines an FDE as an engineer embedded in the customer's environment.

Practise a 60-second answer. Open with the FDE's measure of success: a real system running, used and stable in the customer's production. Then describe a time you handled an incident yourself after a system went live.

## Mistakes to avoid, even if you code well

In the coding round, do not solve in silence. Small problems breed complacency, and the interviewer can only see your thinking through what you say. The code must also be clean enough to hand over, because that is the criterion Scaler Topics mentions.

In the design round, do not draw a handsome agent diagram with nothing to measure. A design without evals, without traces and without a way to detect loops looks more like a demo than a production-ready system.

**Key point:** Show the interviewer what your agent does when it fails, not only when it works.

## How these skills apply on a customer site

The identifier-conversion problem looks like a beginner's exercise. But picture the real version: a mass rename across a customer's large codebase, where you have to report the same work at different levels of detail to different audiences.

The loop-guard function is also what you will need when troubleshooting a live system, the experience the job posting asks for. The traces you store today are what help you find the bug when an agent gets stuck while the customer is using it.

On your CV, write each line of experience to one formula: what system, how long it has run in production, which evals measure it, and which incidents you fixed. If you have built multi-agent systems or ReAct-style loops, say so next to the project name.

On interview day, do not pour your energy into drawing the prettiest agent. Be able to say where your agent once got stuck, and what you did so it would not get stuck again.

**Try this week:**

- Open Google Cloud's FDE Applied AI job posting. For each requirement, write a true project story of your own, no more than 5 sentences each.
- Set a 20-minute timer and solve an identifier-conversion problem while thinking aloud. Then answer 'how would you apply this across the whole repo' twice: a 1-minute version and a 5-minute version.
- Write a one-page agent design for a customer-service problem, including a step limit, loop detection and how you would trace and monitor it in production.

## Sources

- [Forward Deployed Engineer, Applied AI, Google Cloud @ Google | anitab.org Job Board](https://jobs.anitab.org/companies/google-24698/jobs/85932848-forward-deployed-engineer-applied-ai-google-cloud)

- [Google Forward Deployed Engineer (FDE) Interview Guide | Sample Questions (2026)](https://www.aced.io/guides/google-forward-deployed-engineer-interview)

- [Google Forward Deployed Engineer Interview Experience | Interview Experiences - Blind](https://www.teamblind.com/post/google-forward-deployed-engineer-interview-experience-tvnq1sl4)

- [Google Customer Engineer vs Forward Deployed Engineer: What's the Difference?](https://fde.academy/blog/google-customer-engineer-vs-forward-deployed-engineer)

- [Google Forward Deployed Engineer Interview Questions](https://fde.academy/blog/google-forward-deployed-engineer-interview-questions)

- [Google Forward Deployed Engineer: Role, Interview & Salary Guide](https://www.scaler.com/topics/google-forward-deployed-engineer-role-interview-salary-guide/)

- [What Is a Forward Deployed Engineer?](https://www.doit.com/blog/forward-deployed-engineer)