Palantir Machine Learning Engineer Interview Guide

Most ML engineers who stumble in Palantir's loop don't fail on modeling theory. They fail because the software engineering bar is set to "expert," closer to a senior SWE interview at a top-tier tech company than to a typical ML role. If your last three years have been notebooks and prototypes without shipping a Java or Python backend service, that gap surfaces fast.

Palantir Machine Learning Engineer Role

You're building the ML systems inside Foundry and AIP, Palantir's two core platforms. That means fine-tuning LLMs that power AIP actions, designing evaluation harnesses for models deployed across both commercial and classified customer instances, and owning the Foundry data pipelines that feed those models. Success after year one looks like having a model running in production inside a customer's Ontology workflow, being the person FDEs (forward-deployed engineers) call when that model misbehaves in the field.

A Typical Week

The ratio of pure modeling to everything else is what surprises people. Wednesday's eval-and-iterate loop is the closest thing to a "data scientist" day, and even that involves translating field notes from an FDE sitting with a defense client into concrete model fixes. Thursday's research time is real but pointed: you're reading a KV-cache compression paper because it cuts GPU costs across hundreds of customer instances, not for intellectual curiosity alone.

Projects & Impact Areas

Government and commercial work feel like two different jobs sharing a codebase. On the defense side, you might build an NLP entity extraction pipeline that runs in a classified environment where you can't phone home for model updates or telemetry. AIP's commercial surface is expanding fast, with work like constrained decoding via Hugging Face transformers to guarantee JSON-schema-valid outputs when an LLM populates Ontology object properties, or streaming gRPC endpoints so token-by-token responses render in Foundry's UI. Foundry's data layer ties it all together: ML engineers own lineage-aware pipelines that feed their models, not just the model weights.

Skills & What's Expected

Java is the skill most candidates underinvest in. Palantir's backend infrastructure runs on it, and you'll write production services in it alongside your PyTorch work. Statistical rigor carries more weight than the role title suggests, because you'll need to justify modeling choices to teammates who spot a leaky validation split instantly, and you'll need to reason about how data flows through Foundry's ontology layer when designing training pipelines.

Levels & Career Growth

Career growth at Palantir comes from owning a bigger blast radius: more customers, more ambiguous problems, more cross-functional coordination with FDEs and product teams. The thing that blocks people is staying narrow. If you only touch the model layer and never engage with serving infrastructure or a customer's actual Foundry workflow, you'll plateau regardless of your modeling skill.

Work Culture

From what candidates and culture notes report, Denver HQ leans heavily in-office (4-5 days a week), with an open floor plan built for impromptu whiteboarding rather than quiet focus time. Small pods of 4-5 engineers operate with high autonomy, meaning you own everything from data ingestion to model serving, and there's nowhere to hide if your pipeline breaks while three FDEs are pinging you with customer edge cases. The upside is real: you'll ship faster and learn more in a year than at most companies, and the mission-driven culture (defense, government, enterprise decision-making) gives the work a weight that pure ad-optimization roles can't match. But if you need regular deep-focus remote days or a clean 40-hour boundary, be honest with yourself about that tradeoff.

Palantir Machine Learning Engineer Compensation

Palantir's total comp leans heavily on equity, with RSUs forming a substantial portion of the package. Your initial RSU grant matters more than usual because it sets the baseline for your equity exposure across the full vesting period. From what candidates report, both base salary and RSU grant size are negotiable, so come prepared to discuss your desired split across base, bonus, and equity rather than fixating on just one number.

The equity-heavy structure means you're taking on stock price risk that a higher-base, lower-equity package wouldn't carry. Before you sign, model a meaningful downside on the RSU component and ask yourself if the remaining cash comp still feels right. If it doesn't, use competing offers as leverage to shift the mix toward base or push for a sign-on bonus during the initial offer conversation.

Palantir Machine Learning Engineer Interview Process

Four weeks from recruiter call to offer is the typical timeline, which moves faster than you'd expect for a company this size. The most common rejection reason, from what candidates report, is code quality. Palantir explicitly evaluates readability, modularization, and edge case handling, so passing all test cases with messy code won't save you.

From the data, Palantir's "Decomposition" round carries outsized importance because it mirrors how engineers actually scope work inside Foundry's ontology layer. A strong performance there signals you can operate in ambiguity, which is the daily reality of shipping ML into constrained environments. The Hiring Manager Screen probes your understanding of Palantir's specific products and mission, including your perspective on civil liberties and data ethics, so walk in with informed opinions rather than rehearsed generalities.

Palantir Machine Learning Engineer Interview Questions

from typing import List


def longest_balanced_labels(labels: List[int]) -> int:
    """Return length of longest contiguous subarray with equal number of 0 and 1.

    Maps 0 -> -1, 1 -> +1, then looks for farthest pair of equal prefix sums.

    Args:
        labels: List of integers, each must be 0 or 1.

    Returns:
        Maximum length of a contiguous subarray with equal 0s and 1s.
    """
    # prefix_sum -> earliest index where it occurred
    first_idx = {0: -1}
    prefix = 0
    best = 0

    for i, v in enumerate(labels):
        if v not in (0, 1):
            raise ValueError("labels must be 0/1")
        prefix += 1 if v == 1 else -1

        if prefix in first_idx:
            best = max(best, i - first_idx[prefix])
        else:
            first_idx[prefix] = i

    return best


if __name__ == "__main__":
    # Simple sanity checks
    assert longest_balanced_labels([0, 1]) == 2
    assert longest_balanced_labels([0, 1, 0]) == 2
    assert longest_balanced_labels([0, 1, 0, 1]) == 4
    assert longest_balanced_labels([1, 1, 1]) == 0
    print("ok")

from collections import deque
from typing import Deque, Dict


class SlidingWindowRateLimiter:
    """Exact sliding-window rate limiter per key.

    allow(model_id, ts_ms) returns True if the request is allowed and records it,
    else returns False.

    Invariants:
      - For each model_id, timestamps in the deque are strictly nondecreasing
        if inputs are nondecreasing. If inputs can be out of order, this exact
        approach is not valid without additional sorting.
    """

    def __init__(self, k: int, w_ms: int):
        if k < 0:
            raise ValueError("k must be >= 0")
        if w_ms <= 0:
            raise ValueError("w_ms must be > 0")
        self.k = k
        self.w_ms = w_ms
        self._q: Dict[str, Deque[int]] = {}

    def allow(self, model_id: str, ts_ms: int) -> bool:
        if self.k == 0:
            return False

        q = self._q.get(model_id)
        if q is None:
            q = deque()
            self._q[model_id] = q

        # Evict anything strictly older than the window start.
        window_start = ts_ms - self.w_ms
        while q and q[0] <= window_start:
            q.popleft()

        if len(q) >= self.k:
            return False

        q.append(ts_ms)
        return True


if __name__ == "__main__":
    rl = SlidingWindowRateLimiter(k=2, w_ms=1000)
    assert rl.allow("m", 1000) is True
    assert rl.allow("m", 1500) is True
    assert rl.allow("m", 1600) is False  # still 2 within (600, 1600]
    assert rl.allow("m", 2001) is True   # 1000 is out (<= 1001)
    print("ok")

System design and algorithms compound in a way that's specific to how Palantir ships: your system design answer for, say, serving an entity extraction model inside Foundry at p95 < 300ms requires you to reason about the same graph traversal and caching patterns tested in the coding rounds, because Foundry's ontology layer means you're always navigating interconnected objects, not flat tables. The prep mistake that burns candidates here is treating the data pipelines slice as an afterthought, when in practice those lineage and schema-drift questions (meters vs. feet flipping in a defense sensor feed, upstream backfills poisoning a RAG assistant) are where interviewers test whether you've actually operated ML in constrained, air-gapped environments or just trained models on clean benchmarks.

Practice Palantir-style questions across all six areas at datainterview.com/questions.

How to Prepare for Palantir Machine Learning Engineer Interviews

Palantir's north star right now is getting Foundry and AIP into production at enterprise scale. Their Q4 2025 earnings showed 70% year-over-year revenue growth, with U.S. commercial revenue up 137% YoY. For ML engineers, that commercial surge likely means more work shipping AI capabilities inside AIP for enterprise clients alongside the longstanding government deployments.

The "why Palantir" answer most candidates flub is the vague mission pitch. Instead, reference something concrete: maybe the Building End-to-End blog's argument that models must be integrated systems wired into Foundry's ontology layer, or the constraints of deploying in air-gapped environments described in their API evolution post. Then connect that specific Palantir constraint to a problem you've actually solved.

Try a Real Interview Question

from typing import Iterable, List, Sequence, Tuple


def build_metrics(k: int, events: Iterable[Tuple[int, int]]) -> Tuple[List[List[int]], float]:
    """Return the $K \times K$ confusion matrix and micro-F1 after processing all events.

    Args:
        k: Number of classes $K$ with labels in $[0, K-1]$.
        events: Iterable of $(y_true, y_pred)$ integer pairs.

    Returns:
        (confusion_matrix, micro_f1)

    Raises:
        ValueError: If any label is outside $[0, K-1]$ or if $k \le 0$.
    """
    pass

from typing import Iterable, List, Tuple


def build_metrics(k: int, events: Iterable[Tuple[int, int]]) -> Tuple[List[List[int]], float]:
    """Return the K x K confusion matrix and micro-F1 after processing all events.

    Args:
        k: Number of classes K with labels in [0, K-1].
        events: Iterable of (y_true, y_pred) integer pairs.

    Returns:
        (confusion_matrix, micro_f1)

    Raises:
        ValueError: If any label is outside [0, K-1] or if k <= 0.
    """
    if k <= 0:
        raise ValueError("k must be positive")

    cm: List[List[int]] = [[0] * k for _ in range(k)]

    n = 0
    for y_true, y_pred in events:
        if not (0 <= y_true < k) or not (0 <= y_pred < k):
            raise ValueError("label out of range")
        cm[y_true][y_pred] += 1
        n += 1

    if n == 0:
        return cm, 0.0

    tp = 0
    fp = 0
    fn = 0

    for c in range(k):
        tp_c = cm[c][c]
        row_sum = sum(cm[c])
        col_sum = 0
        for r in range(k):
            col_sum += cm[r][c]

        fn_c = row_sum - tp_c
        fp_c = col_sum - tp_c

        tp += tp_c
        fp += fp_c
        fn += fn_c

    denom = 2 * tp + fp + fn
    if denom == 0:
        micro_f1 = 0.0
    else:
        micro_f1 = (2.0 * tp) / float(denom)

    return cm, micro_f1

Palantir's coding rounds carry an expert-level software engineering bar, which is unusually high for an ML engineer title. The decomposition interview format rewards candidates who break ambiguous problems into clean sub-components before writing code. Practice consistently at datainterview.com/coding.

Test Your Readiness

Identify your weak spots, then close the gaps at datainterview.com/questions.