Palantir Interview Guide 2026: Decomp Problems, System Design, and Defense Tech Culture
Palantir has one of the most distinctive interview processes in tech. Instead of LeetCode-style coding questions, they emphasize “decomp” (decomposition) problems — open-ended engineering challenges where you design a software system from scratch in a collaborative session. This guide covers SWE interviews for their commercial (Foundry/AIP) and government (Gotham) divisions.
The Palantir Interview Process
- Recruiter screen (30 min) — mission alignment is crucial; Palantir screens hard for mission fit
- Technical screen (1 hour) — one coding problem + decomp problem discussion
- Onsite “Palantir Day” (5–6 hours, all-day format):
- 2–3× decomp sessions (open-ended system design, live coding, architecture)
- 1× code debugging / bug hunt session
- 1× culture fit / values round
- Optional: lunch with team members (informal evaluation)
What makes Palantir unique: Decomp problems have no single right answer. Interviewers evaluate how you think, communicate tradeoffs, and iterate. Talking through your reasoning is as important as the solution itself.
Decomp Problems: What to Expect
Examples of actual Palantir-style decomp prompts:
- “Design a system to track the real-time location of all military assets for a field commander.”
- “Build a data pipeline that ingests hospital records and flags patients at risk of readmission.”
- “Design an anomaly detection system for financial transaction data.”
- “How would you structure a knowledge graph for an intelligence agency?”
The approach: clarify scope, identify entities and relationships, design data model, define APIs, discuss scalability and security, handle edge cases — all collaboratively.
Knowledge Graph: Core Data Structure
from collections import defaultdict
from typing import Any, Dict, List, Optional, Set, Tuple
class KnowledgeGraph:
"""
Property graph model for connecting entities with typed relationships.
Used in Palantir Gotham for intelligence analysis:
- Entities: Person, Organization, Location, Event, Asset
- Edges: EMPLOYED_BY, LOCATED_AT, PARTICIPATED_IN, OWNS
Palantir's actual implementation uses a distributed graph DB
with provenance tracking (who added this fact, from what source,
with what confidence level).
"""
def __init__(self):
# Nodes: id -> {type, properties}
self.nodes: Dict[str, Dict] = {}
# Edges: stored as adjacency list + reverse index
self.outgoing: Dict[str, List] = defaultdict(list) # src -> [(dst, rel_type, props)]
self.incoming: Dict[str, List] = defaultdict(list) # dst -> [(src, rel_type, props)]
def add_entity(self, entity_id: str, entity_type: str,
properties: Dict[str, Any],
source: str = 'manual',
confidence: float = 1.0):
self.nodes[entity_id] = {
'type': entity_type,
'properties': properties,
'source': source,
'confidence': confidence,
}
def add_relationship(self, src_id: str, dst_id: str,
rel_type: str,
properties: Dict[str, Any] = None,
source: str = 'manual',
confidence: float = 1.0):
if src_id not in self.nodes or dst_id not in self.nodes:
raise ValueError(f"Both entities must exist: {src_id}, {dst_id}")
edge = {
'dst': dst_id,
'type': rel_type,
'properties': properties or {},
'source': source,
'confidence': confidence,
}
self.outgoing[src_id].append(edge)
self.incoming[dst_id].append({'src': src_id, **edge})
def find_paths(self, start_id: str, end_id: str,
max_depth: int = 4) -> List[List[str]]:
"""
Find all paths between two entities up to max_depth hops.
Used for: "How is Person A connected to Organization B?"
BFS to find shortest path; DFS to find all paths.
Time: O(V + E) per level, up to max_depth levels
"""
if start_id not in self.nodes or end_id not in self.nodes:
return []
all_paths = []
stack = [(start_id, [start_id], {start_id})]
while stack:
current, path, visited = stack.pop()
if current == end_id:
all_paths.append(path)
continue
if len(path) > max_depth:
continue
for edge in self.outgoing[current]:
neighbor = edge['dst']
if neighbor not in visited:
stack.append((neighbor, path + [neighbor], visited | {neighbor}))
return all_paths
def subgraph(self, seed_ids: List[str], hops: int = 2) -> 'KnowledgeGraph':
"""
Extract subgraph around seed entities.
Used in Palantir Foundry for "object context" panels.
"""
visited: Set[str] = set()
queue = [(seed_id, 0) for seed_id in seed_ids]
subg = KnowledgeGraph()
while queue:
entity_id, depth = queue.pop(0)
if entity_id in visited or entity_id not in self.nodes:
continue
visited.add(entity_id)
subg.nodes[entity_id] = self.nodes[entity_id]
if depth < hops:
for edge in self.outgoing[entity_id]:
neighbor = edge['dst']
if neighbor not in visited:
queue.append((neighbor, depth + 1))
# Add edge to subgraph if both nodes will be included
if neighbor in visited or depth + 1 List[str]:
"""Find all entities of given type matching optional property filter."""
results = []
for entity_id, data in self.nodes.items():
if data['type'] != entity_type:
continue
if filter_props:
props = data['properties']
if not all(props.get(k) == v for k, v in filter_props.items()):
continue
results.append(entity_id)
return results
Anomaly Detection for Time-Series Data
import statistics
from typing import List, Tuple
class AnomalyDetector:
"""
Statistical anomaly detection for time-series metrics.
Used in Palantir's commercial products for detecting unusual
patterns in financial, operational, or sensor data.
Methods:
1. Z-score (assumes normal distribution)
2. IQR (robust to outliers, non-normal data)
3. Rolling window z-score (detects point anomalies in trends)
"""
def __init__(self, window_size: int = 30, z_threshold: float = 3.0):
self.window_size = window_size
self.z_threshold = z_threshold
def z_score_anomalies(
self,
values: List[float]
) -> List[Tuple[int, float, float]]:
"""
Detect anomalies using global z-score.
Returns: [(index, value, z_score)] for anomalies.
Best for: stationary time series, normally distributed data.
Weakness: sensitive to outliers in mean/std calculation.
"""
if len(values) self.z_threshold:
anomalies.append((i, v, z))
return anomalies
def rolling_z_score(
self,
values: List[float]
) -> List[Tuple[int, float, float]]:
"""
Rolling window z-score: compare each point to local window.
Handles trends and seasonality better than global z-score.
Time: O(N * W) naive, O(N) with running stats.
"""
anomalies = []
for i in range(self.window_size, len(values)):
window = values[i - self.window_size:i]
mean = statistics.mean(window)
std = statistics.stdev(window) if len(window) > 1 else 0
if std == 0:
continue
z = abs(values[i] - mean) / std
if z > self.z_threshold:
anomalies.append((i, values[i], z))
return anomalies
def iqr_anomalies(
self,
values: List[float]
) -> List[Tuple[int, float]]:
"""
IQR-based anomaly detection.
Robust: not affected by the outliers themselves.
Outlier bounds: [Q1 - 1.5*IQR, Q3 + 1.5*IQR]
"""
sorted_vals = sorted(values)
n = len(sorted_vals)
q1 = sorted_vals[n // 4]
q3 = sorted_vals[3 * n // 4]
iqr = q3 - q1
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
return [(i, v) for i, v in enumerate(values)
if v upper]
System Design: Palantir-Style Data Platform
Palantir Foundry ingests data from thousands of sources, transforms it, and serves it to analysts and decision-makers. A common design question: “Design a data platform for an enterprise with 500 disparate data sources.”
"""
Palantir Foundry Architecture (simplified):
Data Ingestion Layer:
- Connectors: REST APIs, databases, files, streaming
- Schema inference + manual curation
- Lineage tracking from source to derived dataset
Transform Layer (Pipeline Builder):
- Spark-based transforms (Python/Java/Scala)
- DAG of datasets: raw -> cleaned -> enriched -> aggregate
- Code repositories with version control + CI
Ontology Layer:
- Maps datasets to domain objects (Person, Asset, Transaction)
- Semantic layer: "this column = date of birth"
- Access control per object type
Application Layer:
- Foundry Apps: user-facing dashboards + workflows
- AIP: LLM-powered actions on ontology objects
- APIs: downstream systems consume enriched data
Key Design Principles:
1. Provenance: every derived fact traces back to source
2. Access control: row/column level, enforced at ontology layer
3. Incremental computation: only recompute affected downstream datasets
4. Audit log: who accessed what data, when, for what purpose
"""
Palantir Culture and Mission Fit
Palantir is controversial (defense contracts, surveillance concerns) and screens for mission alignment harder than any other tech company. You should have a genuine answer to: “How do you think about privacy vs. security tradeoffs?” and “Why do you want to work on government problems?”
Palantir employees genuinely believe their work saves lives (combating terrorism, improving hospital outcomes, optimizing disaster response). If you’re uncomfortable with defense/intelligence use cases, this is not the right fit.
Compensation (SWE I–III, US, 2025 data)
| Level | Title | Base | Total Comp |
|---|---|---|---|
| SWE I | Junior SWE | $140–170K | $200–260K |
| SWE II | SWE | $170–210K | $270–360K |
| SWE III | Senior SWE | $210–250K | $350–480K |
Palantir is publicly traded (NYSE: PLTR). RSUs vest over 4 years. Stock has been volatile; check current price and trajectory when evaluating offers.
Interview Tips
- Practice decomp, not LeetCode: Spend more time on system design and open-ended architecture than on competitive programming
- Think out loud: Palantir’s process rewards communication; silence is negative signal
- Know their products: Read about Gotham, Foundry, Apollo, AIP before interviewing
- Mission research: Read Palantir’s annual reports, CEO letters, and blog posts — they care that you understand the business
- Graph and ML: Knowledge graphs, anomaly detection, and NLP come up often given their product domains
Practice decomp prompts: “Design a system to track supply chain disruptions in real-time,” “Build an evidence management system for a law enforcement agency,” “Design fraud detection for a major bank.”
Related System Design Interview Questions
Practice these system design problems that appear in Palantir interviews:
- Design a Distributed Key-Value Store
- Design a Monitoring and Alerting System
- Design an Ad Click Aggregation System
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