Palantir Technical Interview Prep: Rounds and What to Study

The Palantir technical interview is a recruiter screen, a coding screen, and an onsite loop that usually adds a round most companies don’t have: decomposition, where you break a vague, real-world problem into pieces you could build. Coding rounds are ordinary data structures and algorithms, so prepare those the way you would for any large tech company, then spend extra time on decomposition and on explaining your reasoning out loud.

Palantir doesn’t publish its exact loop, and it varies by role (software engineer, forward deployed engineer, intern). Below, what comes from Palantir is marked as such. The rest is what candidates commonly report, so treat it as approximate and check with your recruiter.

What Palantir says about its own process

Palantir’s careers pages and recruiting blog (as of October 2026) describe it this way:

• It starts with one or two phone interviews of roughly 20 to 45 minutes where you talk about your background and why you want to work there.
• Engineering candidates get a coding question. Palantir expects every engineer to be able to work through one.
• If the phone rounds go well, you move to an onsite. Palantir says the number and format of interviews is tailored to the role and your skills.
• Interviewers ask about what you have actually done, including a real failure and what you learned from it.
• They want to hear your thinking. On a phone screen they can’t see you, so talking is the only way they can follow your reasoning.

Sources: Palantir’s phone interview page and Interviewing at Palantir: Our advice.

Rounds candidates commonly report

Third-party guides and candidate write-ups agree on the broad shape but disagree on round counts and timelines, so don’t plan around a precise number.

• Recruiter screen. A short call about your background and, importantly, why Palantir. Vague answers here do filter people out, so prepare real reasons.
• Technical screen. An online assessment or a live coding call. Reported problems are mostly easy to medium difficulty.
• Onsite. A mix that is often reported to include coding, a system design or re-engineering round, a decomposition round, and sometimes a “learning” round where you pick up an unfamiliar concept or library and explain how you’d use it.
• Hiring manager conversation. Motivation, team fit, and how you think about the work.

Forward deployed engineer loops are reported to weight decomposition and the learning round more heavily than standard software engineer loops.

The decomposition round

You get an open-ended problem, for example a bottleneck in a system, a loosely written design doc, or a scheduling problem, and little guidance. The job is not to find a clever trick. It is to turn fog into a plan, out loud. Candidates who struggle are often technically fine but wait for the interviewer to tell them what to do.

A workable routine:

1. Restate the goal in one sentence and ask who uses the result and what “good” looks like.
2. Pin down inputs and outputs. What data exists, how big, how dirty, how often does it change?
3. List constraints you can already see: latency, correctness, privacy, who can see what.
4. Split into components with clear interfaces, and say what each one takes and returns.
5. Order them. Name the dependencies, pick the smallest version that delivers value, and say what you’d defer.
6. Call out the risks and how you would find out early whether you’re wrong.

Practice it on a prompt like “a hospital wants to know which patients are likely to miss appointments.” You’d ask what data exists (past appointments, reminders sent, demographics), what action the answer drives (who gets a phone call), and what a wrong prediction costs. Then you’d split the work into data cleanup, a simple baseline (for example, past no-show rate per patient), a way to deliver the list to staff, and a way to measure whether calls actually reduced no-shows. Notice that most of that is not code. For more on this skill, see why problem decomposition matters in coding interviews.

Coding: what to study

Expect standard topics. Reported problems lean easy to medium, and interviewers care how clearly you explain the approach, so correct and communicated beats fast and silent.

• Arrays, strings, and hash maps. These cover a large share of screening problems. Practice Group Anagrams II.
• Sorting and intervals, such as merging overlapping ranges.
• Trees and graphs: BFS, DFS, shortest path in an unweighted graph.
• Stacks, queues, and heaps.
• Basic dynamic programming and recursion.

Pick one language and know it well. Python, Java, C++ and JavaScript are all common choices.

Example 1: merge overlapping intervals

Think of merging time ranges when several data sources report the same outage. Sort by start, then extend the last merged range when the next one overlaps it.

def merge_intervals(iv):
    out = []
    for s, e in sorted(iv):
        if out and s <= out[-1][1]:
            out[-1][1] = max(out[-1][1], e)
        else:
            out.append([s, e])
    return out

print(merge_intervals([(8, 10), (1, 3), (2, 6), (15, 18), (6, 7)]))
# [[1, 7], [8, 10], [15, 18]]

Time is O(n log n) for the sort, extra space O(n). Edge cases to say out loud: empty input, intervals that only touch at an endpoint (here they merge, because of <=), and one interval fully inside another.

Example 2: shortest path in an unweighted graph

BFS finds the fewest-edge path. The version below stores one parent per node and rebuilds the path at the end, instead of copying a whole path list into the queue for every node, which wastes time and memory on big graphs.

from collections import deque

def shortest_path(graph, start, end):
    prev = {start: None}
    queue = deque([start])
    while queue:
        node = queue.popleft()
        if node == end:
            path = []
            while node is not None:
                path.append(node)
                node = prev[node]
            return path[::-1]
        for nxt in graph.get(node, []):
            if nxt not in prev:
                prev[nxt] = node
                queue.append(nxt)
    return None

g = {'A': ['B', 'C'], 'B': ['A', 'D', 'E'], 'C': ['A', 'F'],
     'D': ['B'], 'E': ['B', 'F'], 'F': ['C', 'E']}
print(shortest_path(g, 'A', 'F'))             # ['A', 'C', 'F']
print(shortest_path(g, 'A', 'A'))             # ['A']
print(shortest_path({'A': [], 'B': []}, 'A', 'B'))  # None

Time is O(V + E). A good follow-up question from an interviewer is “what if edges have weights?” The answer is Dijkstra’s algorithm with a heap. To practice this family, try Shortest Path with Alternating Colors.

Habits that matter more than the problem

1. Ask clarifying questions about input size, duplicates, and empty input before you write anything.
2. State a simple solution first, with its complexity, then improve it.
3. Talk while you code, and say why you chose each data structure.
4. Trace your code on a small example by hand before saying you’re done.
5. When you get stuck, say what you’ve ruled out instead of going quiet.

System design and data topics

Palantir builds software that integrates and analyzes data from many sources, so questions about data pipelines are a natural fit. Whether you get a classic system design round depends on the role and seniority. Be able to discuss:

• Ingesting data from messy sources and keeping a record of where each value came from.
• Relational vs. non-relational storage, indexes, and what SQL joins cost.
• Scaling reads and writes: caching, partitioning, replication, and the trade-offs of each.
• Access control: who is allowed to see which rows, and how you’d enforce it.
• API design basics: pagination, idempotent writes, error handling.

A typical prompt is “design a system to store and query large amounts of time-series data, such as sensor readings.” Clarify volume, query shapes (recent window vs. long history) and latency needs; then describe ingestion, a storage layout partitioned by time and source, pre-aggregated rollups for summary queries, and a retention policy that downsamples or archives old data. State the trade-off at each step rather than naming a database and stopping. Our post on trade-off analysis in system design interviews goes further.

Behavioral and motivation questions

Palantir says it asks about what you have done, including failures. Prepare four or five real stories you can reshape for different questions: a hard problem you solved, a disagreement, something you learned fast, a decision you made with incomplete information, and a failure. Use Situation, Task, Action, Result, and be specific about your own part and what you would do differently.

Also be ready for “why Palantir?” The company works with governments, defense and intelligence agencies, and large commercial customers, and its work is publicly debated. Read about its products (Gotham, Foundry, Apollo, AIP) on the company’s own site, form an honest view, and be able to say what attracts you and what concerns you. A rehearsed answer is easy to spot.

A prep plan

If you have about six weeks, a split like this works:

1. Weeks 1 to 3: data structures and algorithms, a few problems a day, mostly easy and medium. Review every miss: what pattern did you not recognize? Practicing with step-by-step lessons on AlgoCademy is one option; LeetCode and HackerRank are others.
2. Weeks 3 to 5: two decomposition drills a week. Take a vague real-world request, set a 30-minute timer, and talk through the six-step routine above to a friend or a recording. Add one system design sketch a week.
3. Week 6: two or three mock interviews with a person, behavioral stories written down, and your list of reasons for Palantir finalized.

Books that are commonly recommended: Cracking the Coding Interview (Gayle Laakmann McDowell) for coding and Designing Data-Intensive Applications (Martin Kleppmann) for data systems.

Common mistakes

• Treating decomposition like a coding round and writing code before agreeing on the problem.
• Waiting for the interviewer to hand you a direction. Propose one, then adjust.
• Naming technologies instead of explaining trade-offs.
• Going silent while thinking. Even “I’m comparing two approaches” helps.
• Having no honest answer for why you want this company.

After each interview, write down the questions while you remember them. If you haven’t heard back within the time your recruiter gave, a short, polite follow-up is fine; Palantir itself says it usually responds within a week or two and suggests reaching out otherwise. For more questions to practice, see our Palantir interview questions post.