Palantir Software Engineer Interview Guide

Comprehensive preparation for Palantir Forward Deployed Engineer (FDE) and Backend Engineer roles, with focus on data infrastructure and AI systems.

Interview Process Overview

Timeline: 3-5 weeks, 4-5 rounds

Round	Format	Duration	Focus
Recruiter Screen	Phone	30 min	Background, role fit
Phone Screen	Coding (HackerRank/Karat)	60 min	DS&A
Onsite 1	Coding	45-60 min	Algorithms, data structures
Onsite 2	Decomposition	60 min	Break down ambiguous problems
Onsite 3	System Design	60 min	Data platform architecture
Onsite 4	Behavioral / Values	45 min	Mission, collaboration, impact

Palantir has two main SWE tracks:

• Forward Deployed Engineer (FDE): Client-facing, full-stack, adapts Palantir to customer needs
• Backend / Infra Engineer: Core platform development (Gotham, Foundry, AIP)

Compensation (Reported Ranges)

• New Grad: ~$200-250K total comp
• Senior SWE: ~$350-500K total comp
• Staff+: ~$500-800K total comp
• RSUs with 4-year vesting; Palantir went public so equity is liquid

Key Themes

1. Decomposition is unique to Palantir — The “decomposition” round is their signature interview. It tests your ability to break an ambiguous, real-world problem into technical components.
2. Data platform thinking — Palantir builds data integration and analysis platforms. Think about ontologies, data models, and workflows.
3. Mission-driven — Palantir works with governments and large enterprises. They look for candidates who care about impact and can handle the ethical complexity.
4. Full-stack generalists — Especially for FDE roles, they want engineers who can do frontend, backend, data engineering, and client communication.
5. AI/ML platform — AIP (Artificial Intelligence Platform) integrates LLMs into enterprise workflows. This is their fastest-growing product.

Coding Round

What to Expect

• Standard DS&A, medium to hard difficulty
• Clean code matters — variable naming, modularity, edge cases
• May be on HackerRank (phone) or whiteboard (onsite)

Frequently Reported Problems

Graph Problems (Very Common)

Palantir loves graph problems because their products are fundamentally about entities and relationships.

from collections import defaultdict, deque

class Graph:
    def __init__(self):
        self.adj = defaultdict(set)

    def add_edge(self, u, v, directed=False):
        self.adj[u].add(v)
        if not directed:
            self.adj[v].add(u)

    def shortest_path(self, start, end) -> list:
        """BFS shortest path."""
        queue = deque([(start, [start])])
        visited = {start}
        while queue:
            node, path = queue.popleft()
            if node == end:
                return path
            for neighbor in self.adj[node]:
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append((neighbor, path + [neighbor]))
        return []

    def connected_components(self) -> list[set]:
        """Find all connected components using DFS."""
        visited = set()
        components = []
        for node in self.adj:
            if node not in visited:
                component = set()
                stack = [node]
                while stack:
                    n = stack.pop()
                    if n not in visited:
                        visited.add(n)
                        component.add(n)
                        stack.extend(self.adj[n] - visited)
                components.append(component)
        return components

    def detect_cycle(self) -> bool:
        """Detect cycle in directed graph using DFS coloring."""
        WHITE, GRAY, BLACK = 0, 1, 2
        color = defaultdict(int)

        def dfs(node):
            color[node] = GRAY
            for neighbor in self.adj[node]:
                if color[neighbor] == GRAY:
                    return True
                if color[neighbor] == WHITE and dfs(neighbor):
                    return True
            color[node] = BLACK
            return False

        return any(dfs(n) for n in self.adj if color[n] == WHITE)

Data Transformation / ETL

from typing import Any

class DataTransformer:
    """Transform nested data structures with a rule-based pipeline."""

    def __init__(self):
        self.rules: list[tuple[str, callable]] = []

    def add_rule(self, path: str, transform_fn: callable):
        """Add a transformation rule for a dot-separated path."""
        self.rules.append((path, transform_fn))

    def apply(self, data: dict) -> dict:
        result = self._deep_copy(data)
        for path, fn in self.rules:
            self._apply_at_path(result, path.split('.'), fn)
        return result

    def _apply_at_path(self, data: Any, path_parts: list[str], fn: callable):
        if not path_parts:
            return

        key = path_parts[0]

        if key == '*' and isinstance(data, list):
            for item in data:
                if len(path_parts) == 1:
                    fn(item)
                else:
                    self._apply_at_path(item, path_parts[1:], fn)
        elif isinstance(data, dict) and key in data:
            if len(path_parts) == 1:
                data[key] = fn(data[key])
            else:
                self._apply_at_path(data[key], path_parts[1:], fn)

    def _deep_copy(self, obj):
        if isinstance(obj, dict):
            return {k: self._deep_copy(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [self._deep_copy(item) for item in obj]
        return obj

Interval / Timeline Problems

def merge_timelines(events: list[tuple[int, int, str]]) -> list[tuple[int, int, set[str]]]:
    """Merge overlapping events into unified timeline segments.

    Input: [(start, end, label), ...]
    Output: [(start, end, {labels}), ...] where segments don't overlap
    """
    if not events:
        return []

    # Create boundary events
    boundaries = []
    for start, end, label in events:
        boundaries.append((start, 1, label))   # enter
        boundaries.append((end, -1, label))     # exit
    boundaries.sort(key=lambda x: (x[0], -x[1]))  # exits before enters at same time

    result = []
    active = {}  # label -> count
    prev_time = None

    for time, delta, label in boundaries:
        if prev_time is not None and prev_time < time and active:
            active_labels = {l for l, c in active.items() if c > 0}
            if active_labels:
                result.append((prev_time, time, active_labels))

        active[label] = active.get(label, 0) + delta
        if active[label] == 0:
            del active[label]
        prev_time = time

    return result

Decomposition Round (Palantir-Specific)

What It Is

You’re given a vague, real-world problem and asked to break it down into concrete technical components. This is Palantir’s most distinctive interview round.

Format

1. Interviewer presents a high-level scenario (e.g., “Help a hospital manage patient flow”)
2. You ask clarifying questions to understand the problem
3. You identify the key entities, relationships, and workflows
4. You sketch a technical solution: data model, services, UI components
5. You discuss trade-offs and prioritization

Example: “Build a system to manage disaster response”

Step 1: Clarify

• What type of disasters? (Natural, industrial, etc.)
• Who are the users? (Emergency coordinators, field responders, logistics)
• What decisions need to be made? (Resource allocation, evacuation routing, supply chain)
• What data sources exist? (Satellite, weather, population data, real-time reports)

Step 2: Identify entities (ontology)

Entities:
- Incident (location, type, severity, status)
- Resource (type: personnel/vehicle/supply, location, status, capacity)
- Area (geography, population, infrastructure, risk level)
- Task (description, priority, assigned_to, status, dependencies)
- Report (source, timestamp, content, verified)

Step 3: Identify workflows

1. Situation Assessment
   Input: Reports, sensor data, satellite imagery
   Process: Aggregate, verify, prioritize
   Output: Incident severity map

2. Resource Allocation
   Input: Available resources, incident priorities, geography
   Process: Optimization (minimize response time, maximize coverage)
   Output: Assignment plan

3. Communication
   Input: Decisions, updates
   Process: Route to relevant stakeholders
   Output: Notifications, orders

4. Tracking & Adjustment
   Input: Real-time field updates
   Process: Monitor progress, detect issues
   Output: Re-allocation recommendations

Step 4: Technical architecture

[Data Ingestion Layer]
├── Satellite feed adapter
├── Weather API adapter
├── Field report API (mobile app)
└── Sensor data stream (IoT)
         |
[Data Fusion & Ontology]
├── Entity resolution (deduplicate reports)
├── Geospatial indexing
└── Temporal event correlation
         |
[Analytics / Decision Support]
├── Resource optimization solver
├── Route planning (with road damage awareness)
├── Demand forecasting
└── LLM-powered situation summarization
         |
[UI Layer]
├── Map-based situation dashboard
├── Resource management console
├── Mobile app for field responders
└── Notification system

Step 5: Trade-offs

• Real-time vs. accuracy: Faster updates may be less verified
• Centralized vs. distributed: Single coordinator vs. regional autonomy
• Complexity vs. usability: More features vs. ease of use under stress

Other Decomposition Scenarios

• “Help a city manage its transportation system”
• “Build a system for tracking supply chain across multiple countries”
• “Design a platform for clinical trials management”
• “Help a military organization plan logistics”
• “Build a fraud detection system for a bank”

Tips for Decomposition

1. Ask lots of questions — The problem is intentionally vague. Clarifying shows product thinking.
2. Think in entities and relationships — This maps directly to Palantir’s ontology model.
3. Prioritize ruthlessly — You can’t build everything. What’s the MVP?
4. Consider the human — Who uses this? What decisions do they make? What do they need to see?
5. Data model first — Get the ontology right before thinking about features.

System Design Round

Common Topics

Design a Data Integration Platform

This is literally what Palantir Foundry does:

[Source Systems] --> [Connectors] --> [Data Pipeline]
                                          |
                                    [Schema Mapping]
                                          |
                                    [Entity Resolution]
                                          |
                                    [Ontology Layer]
                                          |
                          +---------------+---------------+
                          |               |               |
                    [Search/Query]  [Analytics]    [Actions/Workflows]

• Schema mapping: Map heterogeneous sources to unified ontology
• Entity resolution: Deduplicate entities across sources (fuzzy matching, ML-based)
• Lineage tracking: Know where every data point came from
• Access control: Row-level and column-level security based on user clearance
• Versioning: Time-travel queries, audit trail

Design an LLM-Powered Enterprise Assistant (AIP)

[User Query] --> [Context Assembly]
                      |
              [Ontology-Grounded RAG]
                      |
              [LLM (Claude/GPT)]
                      |
              [Action Verification]
                      |
              [Tool Execution] or [Response]

• Ontology-grounded: LLM has access to the enterprise data ontology
• RAG (Retrieval Augmented Generation): Pull relevant data objects, not just documents
• Action framework: LLM can propose actions (write data, trigger workflow), but human approves
• Access control: LLM’s view is restricted to the user’s permissions
• Audit trail: Every LLM interaction logged for compliance

Design a Geospatial Analytics System

• Indexing: H3 hexagonal grid or S2 geometry for geospatial queries
• Temporal: Time-series analysis on moving entities
• Visualization: Heatmaps, trajectories, clustering on a map
• Scale: Billions of location events per day

Behavioral / Values Round

Palantir Values

• Mission focus: They build for defense, intelligence, healthcare, and enterprise. Be comfortable with this.
• Impact-driven: “What’s the most impactful thing you’ve worked on?”
• Intellectual rigor: Deep thinkers who care about getting it right.
• Ownership: End-to-end, from data model to deployment.

Common Questions

• “Why Palantir?” (Be genuine. If you’re uncomfortable with defense work, this may not be the right fit.)
• “Tell me about a time you had to make sense of a complex, messy dataset.”
• “Describe a project where you had to balance technical and non-technical stakeholders.”
• “What’s the most complex system you’ve designed end-to-end?”
• “How do you approach a problem you’ve never seen before?”

Ethics / Controversial Topics

Palantir’s work with government agencies is controversial. Be prepared to:

• Articulate your personal stance honestly
• Show you’ve thought about the ethical implications
• Discuss how technology can be used responsibly
• Demonstrate you can engage with hard questions maturely

AI/ML at Palantir

AIP (Artificial Intelligence Platform)

• Enterprise LLM integration with ontology-grounded context
• Tool calling and action execution within enterprise workflows
• Fine-tuning on domain-specific data with privacy constraints
• RAG with structured enterprise data (not just documents)

ML Infrastructure

• Feature engineering on top of Foundry data pipelines
• Model training and deployment within the Foundry platform
• Model monitoring and retraining triggers
• Integration with external ML frameworks (PyTorch, TensorFlow)

Preparation Tips

1. Practice decomposition — Take any real-world problem and break it into entities, workflows, and technical components. Time yourself to 30 minutes.
2. Graph problems — Palantir loves graphs. Practice BFS, DFS, shortest path, connected components, cycle detection.
3. Data modeling — Practice designing schemas for real-world domains (healthcare, supply chain, finance).
4. Read about Palantir’s products — Understand Gotham, Foundry, and AIP at a conceptual level.
5. Prepare impact stories — Every behavioral answer should highlight measurable impact.
6. Think about data quality — Messy, incomplete, conflicting data is the norm. How do you handle it?
7. Form an opinion on their mission — You’ll be asked. Having a thoughtful perspective matters more than the specific stance.

Sources

• Palantir Careers
• Palantir Blog
• Palantir AIP Documentation
• Glassdoor - Palantir SWE Interview Questions
• levels.fyi - Palantir Compensation Data
• r/cscareerquestions, Blind (community reports)