Scale AI AI Engineer Interview Guide

Scale AI's AI Engineer role sits at a strange intersection: you're building the evaluation infrastructure that frontier labs like OpenAI and Anthropic rely on to assess their own models, while simultaneously shipping enterprise AI products to government agencies with strict compliance requirements. From what candidates report, the people who struggle most in this interview are strong coders who can't articulate how they'd design a production RAG system for a customer with messy internal data and zero tolerance for hallucinations.

Scale AI AI Engineer Role

You're joining the AI Platform team to build products on top of Scale's data engine. That means shipping LLM-powered features on the Scale GenAI Platform (SGP), designing evaluation harnesses for the SEAL leaderboard, and wiring up custom retrieval systems for enterprise customers with strict compliance needs. Success after year one looks like owning an entire product surface end-to-end (the multi-model evaluation framework, a government customer's retrieval pipeline) and having it running in production with real users.

A Typical Week

The widget shows the time split, but what it doesn't convey is the constant context-switching between builder mode and customer-facing mode within the same day. You might spend Tuesday morning deep in a retrieval pipeline prototype, then Wednesday morning scoping custom evaluation metrics with a Fortune 100 account team, then Thursday presenting results to leadership in a demo session where you're expected to be data-driven and under eight minutes. If you need long, uninterrupted stretches of focus every day, the rhythm here will feel disruptive.

Projects & Impact Areas

The highest-visibility work involves building RAG systems and AI agents deployed through SGP for enterprise and government customers, which feeds directly into Scale's evaluation infrastructure, where you're designing systems that run identical prompt suites against multiple frontier models and route outputs to Scale's annotation workforce for human preference scoring. Underneath both sits the data quality automation layer: using AI to improve the human-in-the-loop labeling that remains Scale's core revenue engine. Your Tuesday prototype could change how thousands of annotators do their jobs by the following week.

Skills & What's Expected

Business acumen and communication are the most underrated skills for this role. The widget shows software engineering and GenAI both rated expert-level, which candidates expect. What they don't expect is that business acumen and communication are also rated high, because you're often the primary technical point of contact for enterprise customers. You need to translate a vague "we want AI" request into a scoped technical architecture on SGP, then explain your design choices to non-technical stakeholders. Deep math knowledge, by contrast, is only rated medium since you're not deriving loss functions.

Levels & Career Growth

The widget shows the level bands, but here's what matters for your prep: the job posting requires 4+ years of experience, which skews toward Senior-level expectations. At Scale's current stage, what separates Senior from Staff on the AI Platform team isn't just technical depth. It's whether you can own a product surface like the SEAL evaluation platform or a major SGP integration and drive its roadmap without waiting for a PM to hand you specs.

Work Culture

Scale is headquartered on Market Street in SF with a strong in-office culture, and from candidate reports, AI Platform engineers are in-office 4-5 days a week with some flexibility for heads-down remote days. The company's literal core value is "Why Not Faster?" and CEO Alexandr Wang (who founded Scale at 19) sets that tone. The upside is real ownership and speed of impact. The tension is that Scale's customers include the US Department of Defense and frontier AI labs, so quality standards can't slip even when you're shipping fast.

Scale AI AI Engineer Compensation

Scale's RSUs vest over four years with a one-year cliff, which means you're betting a meaningful chunk of your comp on the company's trajectory before you see a dime of equity. As a pre-IPO company, your shares aren't liquid on day one. Ask your recruiter pointed questions about when and how you'd actually be able to realize value from that equity.

On negotiation: the source data confirms that RSU unit counts and sign-on bonuses tend to have more flexibility than base salary. Scale competes directly with frontier AI labs for AI Engineers who can build evaluation infrastructure and ship enterprise AI products (think Scale Donovan, Scale GenAI Platform), so framing your experience around those specific product surfaces gives you more pull than generic "I have another offer" posturing. Come prepared to articulate what you'd build in your first 90 days on one of Scale's actual product lines.

Scale AI AI Engineer Interview Process

The take-home assignment is the real gate. It drops right after the recruiter screen, and based on candidate reports, it involves building something LLM-adjacent or tackling a data preprocessing challenge, not a generic algorithmic exercise. Treat it like production code: clean structure, unit tests, clear documentation explaining your design choices. A sloppy submission ends your process before the onsite loop even gets scheduled.

Where candidates wash out might surprise you. The source data points to several failure modes, but the sneaky one is weak system design thinking for AI-native architectures. You can be sharp on algorithms and still stumble when asked to design a scalable, asynchronous system around an LLM as a black box. Pair that with the behavioral round, which Scale weights more than its short duration suggests. They're filtering for people who can articulate tradeoffs to cross-functional partners, not just write correct code.

Scale AI AI Engineer Interview Questions

from typing import List, Tuple


def merge_token_intervals(intervals: List[Tuple[int, int]]) -> List[Tuple[int, int]]:
    """Merge overlapping or touching token intervals.

    Intervals are inclusive on both ends (start_token, end_token).
    Touching means (a,b) and (c,d) are mergeable when c <= b + 1.

    Args:
        intervals: List of (start, end) with start <= end.

    Returns:
        Sorted, merged list of intervals.
    """
    if not intervals:
        return []

    # Sort by start, then end.
    intervals_sorted = sorted(intervals, key=lambda x: (x[0], x[1]))

    merged: List[Tuple[int, int]] = []
    cur_start, cur_end = intervals_sorted[0]

    for start, end in intervals_sorted[1:]:
        # Overlap or touch.
        if start <= cur_end + 1:
            cur_end = max(cur_end, end)
        else:
            merged.append((cur_start, cur_end))
            cur_start, cur_end = start, end

    merged.append((cur_start, cur_end))
    return merged


if __name__ == "__main__":
    # Basic sanity checks
    assert merge_token_intervals([]) == []
    assert merge_token_intervals([(5, 7)]) == [(5, 7)]
    assert merge_token_intervals([(1, 3), (2, 6), (8, 10), (10, 12)]) == [(1, 6), (8, 12)]
    assert merge_token_intervals([(3, 3), (1, 2)]) == [(1, 3)]

from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Dict, Optional


@dataclass
class _Entry:
    value: Any
    expires_at: float


class TTLCache:
    """In-memory TTL cache with lazy eviction.

    API:
      - get(key, now) -> value or None
      - put(key, value, ttl_seconds, now) -> None

    Average-case time per operation: O(1).
    """

    def __init__(self) -> None:
        self._store: Dict[Any, _Entry] = {}

    def get(self, key: Any, now: float) -> Optional[Any]:
        entry = self._store.get(key)
        if entry is None:
            return None

        # Lazy eviction.
        if now >= entry.expires_at:
            del self._store[key]
            return None

        return entry.value

    def put(self, key: Any, value: Any, ttl_seconds: float, now: float) -> None:
        if ttl_seconds <= 0:
            # Treat non-positive TTL as immediately expired, ensure key is removed.
            self._store.pop(key, None)
            return

        expires_at = now + ttl_seconds
        self._store[key] = _Entry(value=value, expires_at=expires_at)

from __future__ import annotations

import heapq
from collections import Counter, deque
from typing import Deque, Optional


class SlidingMedian:
    """Median over the last k values.

    add(x): adds a value, evicts oldest if size > k.
    median(): returns current median (float for even k), or None if empty.

    Time: O(log k) amortized per add.
    """

    def __init__(self, k: int) -> None:
        if k <= 0:
            raise ValueError("k must be positive")
        self.k = k

        # Max-heap for lower half implemented as negatives.
        self.low = []  # type: list[int]
        # Min-heap for upper half.
        self.high = []  # type: list[int]

        # Delayed deletions by value for each heap.
        self.del_low = Counter()   # type: Counter[int]
        self.del_high = Counter()  # type: Counter[int]

        # Track insertion order for eviction.
        self.q: Deque[int] = deque()

        # Logical sizes excluding delayed deletions.
        self.n_low = 0
        self.n_high = 0

    def _prune_low(self) -> None:
        while self.low:
            x = -self.low[0]
            if self.del_low.get(x, 0) > 0:
                heapq.heappop(self.low)
                self.del_low[x] -= 1
                if self.del_low[x] == 0:
                    del self.del_low[x]
            else:
                break

    def _prune_high(self) -> None:
        while self.high:
            x = self.high[0]
            if self.del_high.get(x, 0) > 0:
                heapq.heappop(self.high)
                self.del_high[x] -= 1
                if self.del_high[x] == 0:
                    del self.del_high[x]
            else:
                break

    def _rebalance(self) -> None:
        # Ensure low has either same number as high or one more.
        self._prune_low()
        self._prune_high()

        if self.n_low > self.n_high + 1:
            self._prune_low()
            x = -heapq.heappop(self.low)
            self.n_low -= 1
            heapq.heappush(self.high, x)
            self.n_high += 1
            self._prune_low()
        elif self.n_high > self.n_low:
            self._prune_high()
            x = heapq.heappop(self.high)
            self.n_high -= 1
            heapq.heappush(self.low, -x)
            self.n_low += 1
            self._prune_high()

    def add(self, x: int) -> None:
        # Insert.
        if not self.low:
            heapq.heappush(self.low, -x)
            self.n_low += 1
        else:
            self._prune_low()
            if x <= -self.low[0]:
                heapq.heappush(self.low, -x)
                self.n_low += 1
            else:
                heapq.heappush(self.high, x)
                self.n_high += 1

        self.q.append(x)

        # Evict if needed.
        if len(self.q) > self.k:
            old = self.q.popleft()
            self._prune_low()
            self._prune_high()

            # Decide which heap logically contains old.
            if self.low and old <= -self.low[0]:
                self.del_low[old] += 1
                self.n_low -= 1
                self._prune_low()
            else:
                self.del_high[old] += 1
                self.n_high -= 1
                self._prune_high()

        self._rebalance()

    def median(self) -> Optional[float]:
        if not self.q:
            return None

        self._prune_low()
        self._prune_high()

        if self.n_low > self.n_high:
            return float(-self.low[0])
        # Even count.
        return (-self.low[0] + self.high[0]) / 2.0

What jumps out isn't any single area dominating, it's how the top two areas create a compounding problem: you'll need to defend an LLM architecture choice (prompting vs. fine-tuning vs. RAG for, say, a Scale Donovan government deployment) and then immediately design the production system around it, including per-tenant access control and human-in-the-loop routing to Scale's labeling workforce. Candidates who prep these as separate topics get caught flat-footed when a system design question assumes fluency in retrieval tradeoffs, or when an LLM question pivots into latency budgets and compliance boundaries. The coding areas, meanwhile, aren't generic puzzles; from what candidates report, they're framed around Scale's actual infrastructure (merging token intervals in a RAG pipeline, building TTL caches for SGP prompt templates), so drilling context-free algorithm problems without practicing applied, product-flavored implementations leaves a real gap.

Sharpen your prep across Scale's specific question mix at datainterview.com/questions.

How to Prepare for Scale AI AI Engineer Interviews

Scale hit $1.5B in revenue with nearly 97% year-over-year growth, and that trajectory maps directly to their announced evolution from data labeling roots into full-stack AI infrastructure. The product surface now includes the Scale Data Engine, the GenAI Platform for enterprises, and Scale Donovan for government and defense use cases. What this means for AI Engineers: your work likely touches both the products Scale sells and the evaluation systems that validate whether those products deliver.

Most candidates blow their "why Scale" answer by anchoring on data labeling. That was the pitch five years ago. The stronger framing is Scale's unusual position as both an AI product company and an AI evaluation company, creating a feedback loop where better evaluation data improves products, which pulls in more customers, which generates richer evaluation signal. Before your interview, read their analysis of AI in the software development lifecycle, which lays out specific failure modes Scale sees when organizations try to move AI from prototype to production (and hints at the kinds of problems you'd be solving).

Try a Real Interview Question

from typing import List, Tuple


def select_passages(passages: List[Tuple[str, int, float]], budget: int) -> List[str]:
    """Return passage ids to include in a RAG prompt within a token budget.

    Args:
        passages: List of (id, tokens, score). ids are unique strings, tokens are positive ints, score is a float.
        budget: Token budget B as a non-negative int.

    Returns:
        List of selected ids in the same relative order as input.
    """
    pass

from __future__ import annotations

from dataclasses import dataclass
from math import isclose
from typing import List, Tuple, Optional


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


def _better(a: Optional[_State], b: Optional[_State]) -> Optional[_State]:
    """Return the better state under the problem's ordering."""
    if a is None:
        return b
    if b is None:
        return a

    # Primary: maximize score
    if not isclose(a.score, b.score, rel_tol=0.0, abs_tol=1e-12):
        return a if a.score > b.score else b

    # Secondary: minimize tokens
    if a.tokens != b.tokens:
        return a if a.tokens < b.tokens else b

    # Tertiary: lexicographically smallest ids list
    return a if a.ids <= b.ids else b


def select_passages(passages: List[Tuple[str, int, float]], budget: int) -> List[str]:
    """Return passage ids to include in a RAG prompt within a token budget.

    Uses 0/1 knapsack DP over the token budget, preserving original order in the output.

    Tie-breaking:
      1) higher total score
      2) lower total tokens
      3) lexicographically smaller list of ids

    Time: O(n * budget)
    Space: O(budget)
    """
    if budget <= 0 or not passages:
        return []

    # dp[t] = best state achieving exactly t tokens (or None if impossible)
    dp: List[Optional[_State]] = [None] * (budget + 1)
    dp[0] = _State(score=0.0, tokens=0, ids=())

    for pid, toks, score in passages:
        if toks <= 0:
            raise ValueError("tokens must be positive")
        # iterate backwards for 0/1 choice
        for t in range(budget - toks, -1, -1):
            prev = dp[t]
            if prev is None:
                continue
            cand = _State(score=prev.score + float(score), tokens=prev.tokens + toks, ids=prev.ids + (pid,))
            dp[t + toks] = _better(dp[t + toks], cand)

    best: Optional[_State] = None
    for t in range(budget + 1):
        best = _better(best, dp[t])

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

Scale's coding questions tend to have real-world framing layered on top of classic algorithm patterns, so pure competitive programming drills won't fully prepare you. Practice medium-to-hard problems under time pressure at datainterview.com/coding, and prioritize variety over grinding one problem type.

Test Your Readiness

Scale's interview skews heavily toward applied AI and production system design, so generic prep leaves gaps. Sharpen your weak spots across every topic area at datainterview.com/questions.