Databricks AI Engineer Interview Guide

Most candidates prepping for this role focus on algorithms and model architecture. The ones who actually get offers can explain how Unity Catalog, MLflow, and Mosaic AI connect into a single stack, and why that matters for the feature they'd be building.

Databricks AI Engineer Role

You're building the intelligence layer of the Databricks Lakehouse. That means shipping production features inside products like Genie (the natural language data querying engine behind AI/BI Dashboards), Databricks Assistant (the AI copilot embedded in notebooks and SQL editors), and compound AI agent systems orchestrated on the lakehouse. Success after year one looks like owning an end-to-end AI feature, say a ReAct-style agent that chains SQL Warehouse calls with self-correction by querying Unity Catalog metadata, and having it running reliably at scale with eval metrics you defined and defend weekly.

A Typical Week

Thursday demo day is the heartbeat of this role's weekly rhythm. You present working prototypes to peers and senior leadership, field hard questions live, then fold that feedback into the next iteration cycle. Monday starts with reviewing eval pipeline results from the weekend (MLflow Evaluate runs comparing MMLU, HumanEval, and internal RAG quality benchmarks), and the middle of the week mixes deep prototyping sessions with cross-functional design reviews alongside the AI/BI product team. It's a role where context-switching between writing Python, debugging Delta pipelines, and reviewing agent evaluation metrics is the norm, not the exception.

Projects & Impact Areas

Genie is probably the highest-visibility project area right now, where you'd design verification steps so the agent checks generated dashboard queries against known metric definitions in Unity Catalog before surfacing results to business users. On a different axis, compound AI systems have you building multi-agent orchestration where one agent handles retrieval, another generates SQL, and a third validates output, all running on the lakehouse with MLflow tracing logging full execution traces. The AI Accelerator Program adds a third flavor: working alongside external startups building on Databricks infrastructure, which gives you unusual customer proximity for an IC role.

Skills & What's Expected

The underrated prep area is infrastructure and model serving. Most candidates over-index on modeling theory and under-index on the operational side: autoscaling policies for Model Serving endpoints (like adjusting min_instances to avoid cold-start latency), cost tradeoffs between serverless and provisioned throughput, GPU cluster provisioning decisions. RAG pipeline design, agentic workflows, and eval-driven development are the current technical frontier for this role, and the interview reflects that. You'll collaborate with PMs on product direction, but you're not owning strategy decks or building dashboards yourself.

Levels & Career Growth

The source data shows a Senior Applied AI Engineer posting that signals where Databricks is actively hiring, and the jump from senior to staff hinges on cross-team scope rather than deeper individual technical work. The promotion blocker candidates report most often is eval methodology: if you can't define and defend how to measure whether a compound AI system is actually working (partial credit scoring for multi-turn agent interactions, for instance), you'll plateau. Lateral moves into ML platform engineering, the Databricks Assistant developer experience team, or customer-facing AI solutions through the AI Accelerator are all realistic paths.

Work Culture

Databricks is recognized as a Most Loved Workplace, and the "customer-obsessed" and "proactive" values show up concretely in the weekly demo and eval-review rituals, where your work is visible to leadership every single week. The day-in-life culture notes suggest the SF HQ AI engineering org clusters in-office Tuesday through Thursday with flexibility on exact days, though the company's official stance on work schedule remains unspecified, so confirm the current policy with your recruiter. Engineers report that the culture rewards output over hours logged, but the intensity during working hours is real.

Databricks AI Engineer Compensation

Equity makes up a significant chunk of Databricks offers, and the source data describes RSUs on a four-year schedule. The biggest risk you should size up is liquidity: until there's a clear path to convert those shares into cash, the equity portion of your total comp is a bet on timing and valuation, not a guaranteed number. Treat the offer-letter projection as a scenario, not a promise.

On negotiation, the initial RSU grant is your highest-leverage knob according to candidate reports, more movable than base salary. Ask about a sign-on bonus to front-load cash in year one while equity vests, and frame every counter in terms of total comp over four years rather than fixating on annual base. That framing aligns with how Databricks structures its packages and keeps the conversation on the component where recruiters have the most flexibility.

Databricks AI Engineer Interview Process

The #1 rejection pattern is inconsistency across the doubled rounds. You might crush the first coding session but stumble on the second when it leans harder into concurrency or multithreading. The panel evaluates you holistically, and one strong round doesn't cancel a weak one.

Most candidates don't realize that Databricks places significant weight on references in the final decision. The two system design rounds test different muscles: one is ML-specific (think model serving pipelines on the lakehouse, feature stores backed by Delta Lake), while the other is classic distributed systems. Prep for both flavors, and choose references who can speak to your technical depth shipping AI features in cross-functional settings, not just people who'll say nice things.

Databricks AI Engineer Interview Questions

from collections import defaultdict
from typing import List


def shortest_full_coverage_window(event_ids: List[str]) -> int:
    """Return length of the shortest contiguous subarray that contains
    all distinct IDs present in the entire list.

    Args:
        event_ids: List of event ID strings.

    Returns:
        Length of the shortest covering window, or 0 for empty input.

    Time: O(n)
    Space: O(k) where k is number of distinct IDs.
    """
    if not event_ids:
        return 0

    target = set(event_ids)
    need = len(target)

    counts = defaultdict(int)
    have = 0
    best = float("inf")

    left = 0
    for right, eid in enumerate(event_ids):
        counts[eid] += 1
        if counts[eid] == 1:
            have += 1

        # Window is valid, try to shrink.
        while have == need and left <= right:
            best = min(best, right - left + 1)
            left_eid = event_ids[left]
            counts[left_eid] -= 1
            if counts[left_eid] == 0:
                have -= 1
            left += 1

    return int(best)

from pyspark.sql import functions as F

source = "uc.catalog.raw.chat_events"
target = "uc.catalog.serving.conversation_latest_state"
checkpoint = "dbfs:/checkpoints/latest_state_cdf"

# Target table holds exactly one row per conversation_id.
# Required columns: conversation_id, last_event_time, last_event_id, last_state_json

cdf = (
  spark.readStream.format("delta")
    .option("readChangeFeed", "true")
    .option("startingVersion", 0)
    .table(source)
    .where("_change_type IN ('insert','update_postimage')")
)

def upsert_latest(microbatch_df, batch_id):
  updates = (
    microbatch_df
      .select(
        F.col("conversation_id"),
        F.col("event_time").alias("incoming_event_time"),
        F.col("event_id").alias("incoming_event_id"),
        F.col("state_json").alias("incoming_state_json")
      )
      .groupBy("conversation_id")
      .agg(
        F.max(F.struct("incoming_event_time", "incoming_event_id", "incoming_state_json")).alias("m")
      )
      .select(
        "conversation_id",
        F.col("m.incoming_event_time").alias("last_event_time"),
        F.col("m.incoming_event_id").alias("last_event_id"),
        F.col("m.incoming_state_json").alias("last_state_json")
      )
  )

  microbatch_df.sparkSession.sql(f"""
    CREATE TABLE IF NOT EXISTS {target} (
      conversation_id STRING,
      last_event_time TIMESTAMP,
      last_event_id STRING,
      last_state_json STRING
    ) USING DELTA
  """)

  updates.createOrReplaceTempView("updates")

  microbatch_df.sparkSession.sql(f"""
    MERGE INTO {target} t
    USING updates s
    ON t.conversation_id = s.conversation_id
    WHEN MATCHED AND (s.last_event_time > t.last_event_time OR (s.last_event_time = t.last_event_time AND s.last_event_id > t.last_event_id))
      THEN UPDATE SET
        t.last_event_time = s.last_event_time,
        t.last_event_id = s.last_event_id,
        t.last_state_json = s.last_state_json
    WHEN NOT MATCHED THEN INSERT (conversation_id, last_event_time, last_event_id, last_state_json)
      VALUES (s.conversation_id, s.last_event_time, s.last_event_id, s.last_state_json)
  """)

(
  cdf.writeStream
    .foreachBatch(upsert_latest)
    .option("checkpointLocation", checkpoint)
    .trigger(availableNow=True)
    .start()
)

CREATE SCHEMA IF NOT EXISTS uc.ai_raw;
CREATE SCHEMA IF NOT EXISTS uc.ai_curated;

-- Raw table with restricted access
CREATE TABLE IF NOT EXISTS uc.ai_raw.llm_interactions (
  request_id STRING,
  user_id STRING,
  prompt STRING,
  tool_output STRING,
  ts TIMESTAMP
) USING DELTA;

-- Redacted view for general access
CREATE OR REPLACE VIEW uc.ai_curated.llm_interactions_redacted AS
SELECT
  request_id,
  user_id,
  regexp_replace(prompt, '(\\b\\d{3}-\\d{2}-\\d{4}\\b)', '[REDACTED]') AS prompt,
  regexp_replace(tool_output, '(\\b\\d{3}-\\d{2}-\\d{4}\\b)', '[REDACTED]') AS tool_output,
  ts
FROM uc.ai_raw.llm_interactions;

-- Permissions
GRANT USAGE ON SCHEMA uc.ai_raw TO `svc_agent_pipeline`;
GRANT SELECT ON TABLE uc.ai_raw.llm_interactions TO `svc_agent_pipeline`;

GRANT USAGE ON SCHEMA uc.ai_curated TO `data_scientists`, `analysts`;
GRANT SELECT ON VIEW uc.ai_curated.llm_interactions_redacted TO `data_scientists`, `analysts`;

-- Optional hardening
REVOKE ALL PRIVILEGES ON TABLE uc.ai_raw.llm_interactions FROM `data_scientists`, `analysts`;

The weight toward agentic AI and production ML design creates a compounding problem most candidates don't anticipate: a single RAG pipeline question can simultaneously test your retrieval chunking logic, your ability to map that design onto lakehouse primitives like Delta Live Tables and Unity Catalog, and your instinct for cost/latency tradeoffs at the serving layer. That overlap means weakness in one area bleeds into your score on another, and the two dedicated system design rounds give the panel enough signal to spot it. The prep mistake this distribution punishes hardest is treating Databricks product knowledge as optional, because even the coding and modeling rounds frame problems inside Databricks-specific contexts (batch-processing Model Serving logs, tuning classifiers for an Assistant-style UI) rather than asking platform-agnostic textbook questions.

Practice questions mapped to each of these topic areas at datainterview.com/questions.

How to Prepare for Databricks AI Engineer Interviews

Databricks is betting its next phase of growth on agentic analytics, the idea that AI agents orchestrated on the lakehouse can make the entire data-to-insight loop accessible through natural language. Their Agent Bricks blog post spells out the architecture: multi-agent ecosystems where Unity Catalog handles governance, MLflow tracks experiments, and Mosaic AI provides the training and serving backbone. Walk into the interview without opinions on how those three pieces compose, and you'll sound like you prepped for a generic ML role.

The "why Databricks" answer that falls flat is some variation of "I love open source and big data." What actually lands is tying yourself to a specific product surface, like improving retrieval quality inside Databricks Assistant or designing eval harnesses for the agentic workflows shipping through AI/BI. Databricks hit $5.4B in annual revenue growing 65% year-over-year, and AI/BI is a visible driver of that trajectory. Show you understand which features feed the growth and where your skills slot in.

Try a Real Interview Question

from typing import Iterable, List, Tuple


def select_passages(passages: Iterable[Tuple[str, int, float]], budget: int) -> List[str]:
    """Select a subset of passage IDs under a token budget.

    Args:
        passages: Iterable of (id, tokens, score).
        budget: Maximum total tokens B.

    Returns:
        Sorted list of selected passage IDs.
    """
    pass

from __future__ import annotations

from dataclasses import dataclass
from typing import Dict, Iterable, List, Optional, Tuple


@dataclass(frozen=True)
class _State:
    score: float
    count: int
    ids: Tuple[str, ...]  # always sorted


def _better(a: Optional[_State], b: Optional[_State]) -> bool:
    """Return True if state a is better than state b under the rules."""
    if b is None:
        return a is not None
    if a is None:
        return False

    if a.score != b.score:
        return a.score > b.score
    if a.count != b.count:
        return a.count < b.count
    return a.ids < b.ids


def _insert_sorted(ids: Tuple[str, ...], new_id: str) -> Tuple[str, ...]:
    """Return a new sorted tuple containing ids plus new_id."""
    lo, hi = 0, len(ids)
    while lo < hi:
        mid = (lo + hi) // 2
        if ids[mid] < new_id:
            lo = mid + 1
        else:
            hi = mid
    return ids[:lo] + (new_id,) + ids[lo:]


def select_passages(passages: Iterable[Tuple[str, int, float]], budget: int) -> List[str]:
    """Select a subset of passage IDs under a token budget.

    Args:
        passages: Iterable of (id, tokens, score).
        budget: Maximum total tokens.

    Returns:
        Sorted list of selected passage IDs.

    Notes:
        Uses 0/1 knapsack DP with explicit tie breaking.
        Complexity is O(n * budget) time and O(budget) states.
    """
    if budget < 0:
        raise ValueError("budget must be non-negative")

    items: List[Tuple[str, int, float]] = []
    seen: set[str] = set()
    for pid, toks, score in passages:
        if not isinstance(pid, str) or pid == "":
            raise ValueError("passage id must be a non-empty string")
        if pid in seen:
            raise ValueError(f"duplicate passage id: {pid}")
        seen.add(pid)
        if not isinstance(toks, int) or toks < 0:
            raise ValueError("tokens must be a non-negative int")
        if toks == 0 and score < 0:
            continue
        items.append((pid, toks, float(score)))

    dp: Dict[int, _State] = {0: _State(score=0.0, count=0, ids=())}

    for pid, toks, score in items:
        if toks > budget:
            continue

        prev = dp
        next_dp: Dict[int, _State] = dict(prev)

        for used_tokens, state in prev.items():
            new_tokens = used_tokens + toks
            if new_tokens > budget:
                continue

            new_ids = _insert_sorted(state.ids, pid)
            cand = _State(score=state.score + score, count=state.count + 1, ids=new_ids)

            cur = next_dp.get(new_tokens)
            if _better(cand, cur):
                next_dp[new_tokens] = cand

        dp = next_dp

    best: Optional[_State] = None
    for state in dp.values():
        if _better(state, best):
            best = state

    return list(best.ids) if best is not None else []

Databricks coding rounds reward fluency with array and string manipulation over niche algorithmic trivia, reflecting the day-to-day reality of writing production Python against Delta tables and model pipelines. The problems feel closer to "transform this nested structure efficiently" than "implement Dijkstra's from memory." Sharpen that muscle at datainterview.com/coding.

Test Your Readiness

Knowing the topic distribution is one thing. Pressure-testing yourself under realistic conditions is where gaps actually surface, so run through questions at datainterview.com/questions.