By FDE Interview Prep, a SourceStrongAI company

In this post11 sections
  1. What Databricks publishes about its interviews
  2. What the AI FDE posting asks for
  3. What one candidate was told about the coding round
  4. The design and architecture round other posters asked about
  5. A baseline model in pandas, narrated from start to finish
  6. Leakage, splits and the metric: the checks to say out loud
  7. Trade-offs in sentences that show judgment
  8. A prep plan for the days before
  9. Questions people ask
  10. Keep reading
  11. More from the blog

You have a Databricks AI interview on the calendar, a résumé full of pipelines and agents, and one line from the recruiter that says “coding round”. Now you are wondering whether to spend the week on graph problems or on the logistic regression you last touched in school. Databricks does not publish what its AI FDE coding round contains, but one candidate reported, in August 2026, being told it focuses on applied data science and traditional machine learning. Source 1Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on RedditSource 2Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on Reddit So prepare to build a baseline model in pandas, prove it has no leakage, pick a metric for a real decision, and say your trade-offs out loud. This post covers the evidence and that prep; for the whole loop, round by round, read the FDE interview guide.

Skip to the worked example if you already know the evidence.

What Databricks publishes about its interviews

Databricks’ careers pages describe a company-wide process that generally includes a recruiter call, a pre-onsite screen, an onsite loop of typically four to six interviews, and a presentation for some roles. Source 3Clear Interview Process and Insider Insights | DatabricksPublisherDatabricksSource typecompany hiring page That is the general process for every role, not a description of the AI FDE loop.

The nearest role-specific loop Databricks publishes belongs to a different role: a Delivery posting in Tokyo lists a recruiter screen, a hiring manager screen, “Design & Architecture”, “Vibe Coding”, “Build, Demo & Delivery” and a reference check. Source 4Delivery Solutions Architect (Tokyo, Japan)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting Read it as a hint about how Databricks names rounds, not as your schedule.

One practical tip: Databricks’ interview best practices tell candidates not to use their current work computer or their current employer’s materials when creating a candidate assignment or presentation, so if you get a take-home, build it on your own machine. Source 3Clear Interview Process and Insider Insights | DatabricksPublisherDatabricksSource typecompany hiring page

Our lesson on the evidence map shows how to keep what a company publishes apart from what candidates report.

What the AI FDE posting asks for

As of September 2026, Databricks posts a role titled “AI Engineer – Forward Deployed Engineering (AI FDE)” in the United States, marked “(ALL LEVELS)” and open to remote locations. Source 5AI Engineer – Forward Deployed Engineering (AI FDE)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting The same posting says the role “is intended for experienced engineers with demonstrated industry experience designing, building, and deploying production GenAI and LLM applications at scale” and “is not intended for internship, new graduate, or entry-level applicants.” Source 5AI Engineer – Forward Deployed Engineering (AI FDE)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting In our reading, “all levels” spans experienced levels only: if you have not shipped a GenAI or LLM application to production, this posting is not aimed at you, and the entry-level report below is most likely for a different role.

Databricks describes the AI FDE team as a highly specialized customer-facing AI team that delivers professional services engagements to help customers build and productionize first-of-its-kind AI applications. Source 5AI Engineer – Forward Deployed Engineering (AI FDE)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting AI FDE postings ask for experience building GenAI applications, including RAG, multi-agent systems, Text2SQL and fine-tuning, with tools such as HuggingFace, LangChain and DSPy, and list the Databricks platform and Apache Spark as preferred rather than required. Source 5AI Engineer – Forward Deployed Engineering (AI FDE)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting

So the posting reads GenAI-first, while the coding round one candidate was told about reads classical. In our reading, those fit together. A customer’s first question about any model, an LLM included, is “how do you know it works?”, and a baseline with an honest evaluation is how you answer it. If you want the GenAI side of the same judgment, our post on fine-tuning vs RAG vs prompting covers it. For how this role compares with Databricks’ resident solutions architects, read Databricks resident solutions architect vs FDE.

What one candidate was told about the coding round

This is the whole of the direct evidence. One candidate for Databricks’ AI FDE role reported, in August 2026 and in a UK careers subreddit, that before their interview they were told that “the coding round focuses on applied data science and traditional machine learning”, and wrote in a follow-up comment that it is the only coding round in the process. Source 1Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on RedditSource 2Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on Reddit

That is one person, relaying what they were told, before the round happened. It is still the most specific signal available, and it points somewhere concrete: data in a DataFrame, a model you can explain, and an evaluation you can defend.

What it does not tell you: the language, whether AI tools are allowed, whether you get a notebook or a blank editor, and how long you have. Ask. Send the recruiter these:

Questions for the recruiter

  • What does the coding round cover, and is it the only coding round?
  • Which language and environment: a notebook, an IDE or a shared editor?
  • pandas or PySpark? (The posting lists Spark as preferred, not required.)
  • Can I use libraries such as pandas and scikit-learn, and can I look up documentation?
  • Are AI coding tools allowed in this round?
  • Is there a design and architecture round, and who runs it?

Keep some algorithm practice warm anyway. Formats differ by role and change over time, and our post on whether FDE interviews have LeetCode shows how much they vary by employer.

A report from a different Databricks role

One candidate reported, in August 2026 on Aced (formerly Exponent), on an entry-level (L3) Databricks Forward Deployed Engineer loop. Source 6Databricks Forward Deployed Engineer Interview ExperiencePublisherAced (formerly Exponent)Source typecandidate’s personal write-up That is unlikely to be the AI FDE role as posted, since its posting says it is not for entry-level applicants. Source 5AI Engineer – Forward Deployed Engineering (AI FDE)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting One habit that candidate described still transfers: treating the interviewer “almost like a client” and clarifying stakeholder, scope and KPI before touching architecture. Source 6Databricks Forward Deployed Engineer Interview ExperiencePublisherAced (formerly Exponent)Source typecandidate’s personal write-up

The design and architecture round other posters asked about

One candidate reported, in September 2026, that their Databricks FDE process included a design and architecture round, and asked what to expect; the same words appear on Blind and on Reddit, so count them as one poster. Source 7FDE interview at DatabricksPublisherBlind (teamblind.com)Source typecandidate report on BlindSource 8FDE interview at Databricks (post by u/LongjumpingBit6900)PublisherReddit r/leetcodeSource typecandidate report on RedditSource 9FDE interview at Databricks (comment by u/LongjumpingBit6900)PublisherReddit r/leetcodeSource typecandidate report on Reddit The poster named the role as Databricks FDE, not AI FDE. One commenter on Blind, who does not say how they know, replied that the round is “mostly” about building a full-stack distributed system, focused on “FDE mindset rather than what tools you are using”, and covering both data engineering and ML; treat that as one unverified answer. Source 7FDE interview at DatabricksPublisherBlind (teamblind.com)Source typecandidate report on Blind For your coding prep, the bridge is simple: the model you build is what a design round would ask you to put into production, so be ready to say where the features are computed, how often scoring runs, who reads the output, and how you would notice it going stale.

A baseline model in pandas, narrated from start to finish

Here is a practice prompt in the shape one candidate was told to expect. The data is invented.

A software company gives you events.csv with account_id, ts and event (signup, login, ticket, cancel). Build a churn model and tell us whether it is good enough to use.

Don’t open pandas first. Open with the decision:

“Who acts on this score? Say the customer success team calls at-risk accounts, and they can make about 100 calls a month. So on the first of each month I score every active account for the chance it cancels before the next month starts, and I judge the model by how many of its top 100 actually cancel.”

That sentence fixes the label, the prediction date, the horizon and the metric before any code. Now build features as of a date:

import numpy as np
import pandas as pd

# columns: account_id, ts, event
ev = pd.read_csv("events.csv", parse_dates=["ts"])

def snapshot(at, horizon=pd.DateOffset(months=1)):
    past = ev[ev.ts < at]  # features see only the past
    gone = past.loc[past.event == "cancel", "account_id"]
    ids = past.account_id.unique()
    X = pd.DataFrame(index=ids).drop(gone)
    logins = past[past.event == "login"]
    last = logins.groupby("account_id").ts.max()
    X["days_idle"] = (at - last).dt.days
    recent = past[past.ts >= at - pd.Timedelta(days=30)]
    lg = recent[recent.event == "login"]
    tk = recent[recent.event == "ticket"]
    X["logins_30d"] = lg.groupby("account_id").size()
    X["tickets_30d"] = tk.groupby("account_id").size()
    X = X.fillna({"days_idle": 365, "logins_30d": 0,
                  "tickets_30d": 0})
    c = ev[ev.event == "cancel"]
    ahead = c[(c.ts >= at) & (c.ts < at + horizon)]
    return X, X.index.isin(ahead.account_id).astype(int)

Narrate as you type: “Features only see events before at. The label only sees the month after it. Accounts that already canceled are out, because nobody calls them.”

If you get a Databricks notebook, the same features are a groupBy("account_id").agg(...) on a Spark DataFrame; say you would prototype in pandas on a sample and move to Spark for the full table.

Then train on earlier months, test on a later one, and put a baseline beside the model:

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

months = ["2026-03", "2026-04", "2026-05", "2026-06"]
train = [snapshot(pd.Timestamp(m)) for m in months]
X_tr = pd.concat([x for x, _ in train])
y_tr = np.concatenate([y for _, y in train])
X_te, y_te = snapshot(pd.Timestamp("2026-07"))

def report(name, score, k=100):
    order = np.argsort(-np.asarray(score), kind="stable")
    top = y_te[order[:k]]
    auc = roc_auc_score(y_te, score)
    print(f"{name:<9} auc={auc:.2f} "
          f"precision@{k}={top.mean():.2f}")

model = LogisticRegression(max_iter=1000)
model.fit(np.log1p(X_tr), y_tr)
print(f"accounts={len(y_te)} base_rate={y_te.mean():.3f}")
report("rule", X_te.days_idle)
p = model.predict_proba(np.log1p(X_te))[:, 1]
report("logistic", p)

On our synthetic data it prints:

accounts=4977 base_rate=0.053
rule      auc=0.76 precision@100=0.26
logistic  auc=0.81 precision@100=0.35

And you say: “Calling at random reaches a churner 5 times in 100. The rule the team would use without me, most idle days first, reaches 26. The model reaches 35. The last training label window closes on the first of July, the day the test starts, so no test month leaks back.”

The rule is the part most people skip. A model that beats the base rate proves little. A model that beats the one-line rule the team would use anyway is worth shipping. The full version, with calibration by quintile and a second test month, is in the baseline churn model question.

Leakage, splits and the metric: the checks to say out loud

A good score is where your scrutiny starts. Break your own pipeline on purpose and show you know why it breaks:

# The leak: idle days measured at the end of the table.
logins = ev[ev.event == "login"]
last = logins.groupby("account_id").ts.max()
idle_end = (ev.ts.max() - last).dt.days

def leak(X):
    d = idle_end.reindex(X.index).fillna(365)
    return X.assign(days_idle=d.to_numpy())

X_lk = pd.concat([leak(x) for x, _ in train])
leaky = LogisticRegression(max_iter=1000)
leaky.fit(np.log1p(X_lk), y_tr)
p = leaky.predict_proba(np.log1p(leak(X_te)))[:, 1]
print(f"leaky auc={roc_auc_score(y_te, p):.2f}")

It prints leaky auc=0.94, up from 0.81, on the same time split with the same months, so the only change is that one feature. “A canceled account stops logging in, so idle days measured at the end of the table are reading the label. If I ever see a score like this, I treat it as a bug until I find the leak.”

These are the checks to name, each in one sentence:

Checks to say out loud

  • Features come only from events before the prediction date, and the label only from after it.
  • The split is by time: train on earlier snapshots, test on a later one, and every training label window closes before the test starts.
  • The same account can sit in train and test at different dates; that matches how the model is used, so the date cut is the leak that matters.
  • Accuracy is off the table when churn is rare: predicting that nobody leaves scores 0.947 here and helps no one.
  • The metric matches the decision: precision in the top 100 because the team makes about 100 calls.
  • The model beats the rule, not just the base rate.

The same leak appears in time series. Computing a mean over the whole series uses future readings, and including the current reading lets a spike inflate its own denominator. The rolling z-score question drills exactly that, and it is a good second prompt to practice. If the round turns toward evaluating a model rather than building one, try the eval script question.

Trade-offs in sentences that show judgment

Our method grades a practice build on your reasoning as much as on your code, so say each choice with its cost. Here are sentences to adapt:

  • Logistic regression first. “I start with logistic regression because the team can read its coefficients, and here they make sense: fewer recent logins and more tickets raise the risk. I’d try gradient boosting next and keep it only if it beats this on the same time split.”
  • Ranking, not probability. “For a call list I only need the order to be right. If finance wants to forecast lost revenue from these scores, I’d check calibration first.”
  • Simple features. “Three features anyone can explain. Seat counts and billing events would likely help, and I’d add them once the calls prove they save accounts.”
  • Good enough for what. “It’s good enough to rank a monthly call list. It isn’t good enough to trigger discounts automatically, because most of its top 100 would have stayed.”
  • Finding churners is not saving them. “I’d hold out a random slice of each month’s top list that nobody calls. If called and uncalled accounts churn at the same rate, the model finds churners but the calls don’t save them, and that’s the number the business cares about.”

Common mistakes, and the fix for each:

MistakeFix
Coding before asking who uses the scoreName the decision and its capacity first
A random row splitSplit by snapshot date
Reporting accuracyReport precision in the top slice, beside the rule
Trusting a near-perfect scoreHunt for the leak before you celebrate
“It’s good”“It’s good enough for this decision, and here’s how we’d check”

A prep plan for the days before

Our lesson on what FDE coding rounds test covers the other coding formats; this plan is for the one this candidate was told about.

  1. Day 1: the baseline, timed. Write the snapshot function and the time split from a blank file on a synthetic table, narrating aloud. Do it until it takes no thought.
  2. Day 2: leaks on purpose. Add a leaky feature (idle days measured at the end of the table), a feature window that includes the prediction date (<= instead of <), and a random row split across months. Watch what each does to the score, and practice the one sentence that explains why it is a leak even when the score barely moves.
  3. Day 3: metrics for decisions. Take one model and answer three different decisions with it: a call list, an automatic action and a revenue forecast. Say which metric fits each.
  4. Day 4: the second prompt. Do the rolling z-score question, then read dataset-first prompts for how to scope a table you have never seen.
  5. Day 5: production. Sketch where the baseline runs: the feature table, the scoring schedule, the monitoring. That is your bridge into a design round.
  6. Day 6: the rest of the loop. Rehearse one story for “tell me about a time you solved a difficult technical problem”: one Databricks FDE candidate reported, in September 2026, that their hiring manager round asked open-ended questions like it. Source 8FDE interview at Databricks (post by u/LongjumpingBit6900)PublisherReddit r/leetcodeSource typecandidate report on RedditSource 9FDE interview at Databricks (comment by u/LongjumpingBit6900)PublisherReddit r/leetcodeSource typecandidate report on Reddit Then run the free practice case to practice scoping out loud with a customer who does not hand you the requirements.

Then do the full baseline churn model question against a timer and compare your narration with the model answer, which adds calibration and a second test month. That question is part of Pro, which starts with a 7-day free trial.

GlossaryForward deployed engineerA software engineer who builds and ships production systems inside a customer’s problem and environment, accountable to that customer’s outcome.More on Forward deployed engineerGlossaryRetrieval-augmented generationAnswering with a model that is given passages retrieved from a document collection as context.More on Retrieval-augmented generationGlossarySolutions architectA customer-facing technical role that designs, advises and often builds proofs of concept; postings place it before the sale, after it, or both.More on Solutions architect

Questions people ask

What did one candidate report about the Databricks AI FDE coding round?

Databricks does not publish its content. One candidate for the AI FDE role reported, in August 2026, being told before the interview that the coding round focuses on applied data science and traditional machine learning. Prepare for that, and keep some algorithm practice in case your round differs.Source 1Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on RedditSource 2Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on Reddit

Does Databricks hire new grads as AI FDEs?

As of September 2026, the US AI FDE posting says the role is for experienced engineers who have built and deployed production GenAI and LLM applications, and is not intended for internship, new-graduate or entry-level applicants.Source 5AI Engineer – Forward Deployed Engineering (AI FDE)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting

What does Databricks publish about its interview process?

Databricks’ careers pages describe a company-wide process that generally includes a recruiter call, a pre-onsite screen and an onsite loop of typically four to six interviews, with a presentation for some roles. The nearest role-specific loop it publishes is for a different role, Delivery Solutions Architect, not the AI FDE.Source 3Clear Interview Process and Insider Insights | DatabricksPublisherDatabricksSource typecompany hiring pageSource 4Delivery Solutions Architect (Tokyo, Japan)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting

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