Palantir Coding Interview Questions: Process, Topics & Prep

Palantir's coding interview has a reputation for being different from the usual algorithm grind — and the reputation is earned. Alongside conventional data-structures-and-algorithms screens, you'll typically face the well-known Decompositions interview (often shortened to "Dec"), where you model a real-world system into objects, relationships, and APIs. The company builds software for genuinely ambiguous domains, so it screens for engineers who can turn a vague problem into clean, workable structure.

This guide walks through Palantir's interview process, the representative problem types you should prepare for (no fabricated leaked questions here), what each round actually assesses, and a focused prep plan — especially for the Decompositions round, which trips up strong algorithm solvers who have never practiced modeling.

The full interview process

Stage	Format	Notes
Recruiter screen	30 min	Background, role, team match, logistics
Technical screen(s)	45-60 min	1-2 DS&A problems in a shared editor
Decompositions ("Dec")	45-60 min	Model a real-world domain into objects/APIs
System design (senior)	45 min	Scaling, data flow, trade-offs for senior roles
Behavioral / values fit	45 min	Ownership, collaboration, dealing with ambiguity

The exact shape varies by team, level, and whether you're interviewing for a software engineer or a forward-deployed engineering role. Treat the table as the common skeleton, not a guarantee.

Representative DS&A topics

The standard technical screens look familiar if you've prepped for any large tech company — mostly easy-to-medium problems, with the bar set on clarity and correctness rather than exotic tricks. The recurring areas:

• Arrays and strings: two pointers, sliding window, frequency counting, in-place transformations.
• Hash maps and sets: grouping, deduplication, lookups — the everyday workhorses of pragmatic code.
• Trees and recursion: traversals, path problems, building or validating structure.
• Graphs: BFS and DFS, connected components, shortest paths, grid traversal. Graph thinking shows up often because Palantir's domains are relationship-heavy.
• Practical problem decomposition: multi-part problems where you parse messy input, build an intermediate structure, then answer queries against it.

If you want a structured way to cover this ground, our roundup of LeetCode patterns for interviews maps the handful of templates that carry most medium problems. Aim for fluent, readable solutions you can explain — Palantir interviewers consistently weight clean code and clear reasoning over the fastest possible asymptotic win.

The Decompositions interview, explained

This is the round people remember. Instead of an algorithm, you're handed a real-world system to model: an elevator bank, a vending machine, a parking garage, a ride dispatcher, a library catalog. Your job is to decompose it into objects, define their responsibilities, and design the APIs they expose to one another. The interview is a guided conversation, and you drive it.

What a good Decompositions answer looks like

• Clarify scope first. Ask what's in and out. An elevator system that schedules across a building is a very different model from a single-car controller. Pin this down before drawing classes.
• Identify the core entities. Name the nouns — Elevator, Request, Floor, Scheduler — and give each a clear, single responsibility.
• Design the interfaces between them. How does a Request reach the Scheduler? What does the Scheduler call on an Elevator? Sketch method signatures, not just boxes.
• Choose the data structures deliberately. A priority queue for pending requests, an enum for direction, a state machine for car status — say why each fits.
• Handle the edges out loud. Two requests on the same floor, a car going out of service, a tie in scheduling. Showing you anticipate these is half the signal.
• Write pragmatic code. Once the model is agreed, implement a slice of it cleanly. The interviewer cares that your classes are extensible and your naming reads like production code.

A rough skeleton for a dispatcher-style problem looks like this — entities with clear boundaries, not a single procedural blob:

from dataclasses import dataclass
from enum import Enum

class Direction(Enum):
    UP = 1
    DOWN = -1
    IDLE = 0

@dataclass
class Request:
    origin: int
    destination: int

class Elevator:
    def __init__(self, car_id):
        self.car_id = car_id
        self.floor = 0
        self.direction = Direction.IDLE
        self.targets = set()
    def assign(self, request):
        self.targets.add(request.destination)
    def step(self):
        # advance one floor toward the nearest target
        ...

class Scheduler:
    def __init__(self, cars):
        self.cars = cars
    def dispatch(self, request):
        best = min(self.cars, key=lambda c: self._cost(c, request))
        best.assign(request)
        return best

Notice what's being tested: the algorithm inside _cost matters far less than whether Scheduler, Elevator, and Request have crisp boundaries you can extend. If the interviewer adds "now support express elevators," a clean model absorbs the change in one place. That extensibility is the whole point.

What Palantir actually assesses

Signal	Where it shows up	What strong looks like
Modeling & abstraction	Decompositions	Clean entities, single responsibilities, extensible APIs
Communication	Every round	Thinks aloud, asks sharp questions, takes feedback
Pragmatic code	DS&A & Dec	Readable, correct, maintainable — not clever for its own sake
Handling ambiguity	Decompositions & behavioral	Scopes the problem, makes reasonable assumptions explicit

The throughline is judgment. Palantir hires engineers who take a messy, under-specified problem and produce something workable and clear. Raw algorithm speed helps, but it's not the differentiator the way modeling and communication are.

Common pitfalls specific to Palantir

• Jumping to code in Decompositions. Candidates who start typing before agreeing on entities and scope almost always paint themselves into a corner. Model first, code a slice second.
• Over-engineering the model. Twelve classes for a vending machine signals poor judgment as much as too few. Match the abstraction to the stated scope.
• Going silent. The Dec round is a conversation. If you design in your head and reveal the finished diagram, you've thrown away most of the signal.
• Treating clean code as optional in the DS&A round. A correct but unreadable solution scores worse here than at shops that only care about the optimal Big-O.

A 3-week prep plan for a Palantir loop

1. Week 1: DS&A fundamentals — arrays, strings, hash maps, trees, and especially graphs (BFS, DFS, connected components). Solve 20 easy-to-medium problems and rewrite each one for readability.
2. Week 2: Decompositions drills. Model 8-10 classic systems from scratch — elevator, vending machine, parking lot, library, ride dispatcher, in-memory key-value store. Out loud, with entities and APIs before code. Senior candidates should add the system design cheat sheet for the scaling round.
3. Week 3: Mixed rehearsal — alternate a timed DS&A problem with a timed Decompositions problem, then prepare behavioral stories on ownership and working through ambiguity. Finish with a full live-coding rehearsal on the platform you'll actually use.

FAQ

What is the Palantir Decompositions interview?

The Decompositions (or Dec) interview asks you to model a real-world problem — an elevator system, a vending machine, a ride dispatcher — into classes, objects, and APIs. It tests abstraction and design judgment rather than algorithmic tricks. You drive a conversation about entities, relationships, and interfaces, then write or sketch pragmatic code.

Does Palantir ask LeetCode-style problems?

Yes. Standard technical screens include data-structures-and-algorithms problems — arrays, strings, hash maps, trees, and especially graphs — usually in the easy-to-medium range. The differentiator is the Decompositions round and an emphasis on clean, readable, practical code over clever one-liners.

How many rounds is the Palantir interview loop?

Typically a recruiter screen, one or two technical screens with DS&A, the Decompositions modeling interview, and behavioral or values-fit conversations. Senior candidates may also get a system design round. The exact shape varies by team and role.

What does Palantir assess in coding interviews?

Modeling and abstraction (can you decompose a messy domain cleanly), communication (do you think aloud and take feedback), and pragmatic code (readable, correct, maintainable). Palantir values engineers who turn ambiguous real-world problems into workable software, so judgment matters as much as raw algorithm speed.

Can CoPilot Interview help me prepare for Palantir?

Yes — it's built for preparation and real-time support. It returns structured solutions with Big-O and clean class breakdowns in about four seconds, which makes it a strong rehearsal partner for both DS&A and Decompositions practice. Use it to prepare, and always follow Palantir's stated interview rules.