Palantir
Big data analytics company for defense, intelligence, and enterprise.
5 Rounds
~28 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you had to push back on a client's technical request because it was scientifically invalid or technically infeasible.
#Client Interaction
#Communication
#Pushback
Machine Learning Engineer
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Behavioral
•
hard
Describe a situation where you were deployed to a client site and had to build an ML solution with highly ambiguous or changing requirements.
#Ambiguity
#Adaptability
#Forward Deployed
Machine Learning Engineer
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Behavioral
•
medium
Tell me about a time you had to dive deep into a complex, undocumented codebase or data silo to extract value for a project.
#Problem Solving
#Tenacity
#Code Comprehension
Machine Learning Engineer
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Behavioral
•
medium
How do you handle a situation where your machine learning model performs exceptionally well in offline testing but fails dramatically in production?
#Troubleshooting
#Accountability
#Production ML
Machine Learning Engineer
•
Behavioral
•
easy
Describe a time you had to explain a complex ML concept, like model interpretability or false positive rates, to a non-technical stakeholder.
#Communication
#Stakeholder Management
#Empathy
Machine Learning Engineer
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Behavioral
•
medium
What is your approach to ensuring data privacy and security when building ML models on sensitive or classified datasets?
#Security
#Ethics
#Data Privacy
Machine Learning Engineer
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Behavioral
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hard
Tell me about a time you took ownership of a failing project, identified the root cause, and turned it around.
#Ownership
#Leadership
#Resilience
Machine Learning Engineer
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Behavioral
•
easy
Why Palantir? What specifically draws you to our mission, our Forward Deployed model, and products like Foundry or Gotham?
#Motivation
#Company Knowledge
#Mission Alignment
Machine Learning Engineer
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Coding
•
medium
Given a list of flight routes, determine if there is a route from city A to city B with at most K stops.
#Graphs
#BFS
#Shortest Path
Machine Learning Engineer
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Coding
•
medium
Given a list of data transformation jobs and their dependencies, write a function to determine if all jobs can be executed, or if there is a circular dependency.
#Graphs
#Topological Sort
#DFS
Machine Learning Engineer
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Coding
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medium
Implement a key-value store with expiration times, similar to an in-memory cache used for fast feature retrieval in an ML pipeline.
#Hash Map
#Doubly Linked List
#Caching
Machine Learning Engineer
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Coding
•
easy
Given a list of intervals representing periods where a server was under high load, merge overlapping intervals to find the total downtime.
#Arrays
#Sorting
#Intervals
Machine Learning Engineer
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Coding
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hard
Write an algorithm to find the lowest common ancestor of two nodes in a Directed Acyclic Graph (DAG), representing data lineage in Palantir Foundry.
#Graphs
#DAG
#BFS
#DFS
Machine Learning Engineer
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Coding
•
medium
Given an undirected graph representing a network of bank accounts and transactions, find all isolated subgraphs to identify potential fraud rings.
#Graphs
#Connected Components
#Union Find
Machine Learning Engineer
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Coding
•
hard
Implement an algorithm to match a set of available ML models to a set of edge devices based on memory constraints and model sizes to maximize deployment.
#Greedy
#Bipartite Matching
#Sorting
Machine Learning Engineer
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Coding
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hard
Given a stream of real-time sensor data, implement a sliding window maximum to keep track of the peak temperature over the last N seconds.
#Sliding Window
#Deque
#Queues
Machine Learning Engineer
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Coding
•
medium
Write a function to parse a highly nested JSON log file and extract specific error codes based on a dynamic set of rules.
#Tree Traversal
#Recursion
#String Parsing
Machine Learning Engineer
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Coding
•
medium
Implement a rate limiter for an API endpoint that serves ML predictions, ensuring no single client exceeds X requests per minute.
#Concurrency
#Queues
#System Design
Machine Learning Engineer
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Coding
•
hard
Given an array of strings representing a sequence of user actions, find the longest repeating contiguous sequence to identify bot behavior.
#Dynamic Programming
#Suffix Arrays
#Strings
Machine Learning Engineer
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Coding
•
hard
Write a program to evaluate a boolean expression represented as a string, often used in our ontology access control checks.
#Stacks
#Parsing
#Strings
Machine Learning Engineer
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System Design
•
hard
Design a real-time anomaly detection system for a massive stream of financial transactions, ensuring sub-second latency.
#Stream Processing
#Machine Learning
#Scalability
Machine Learning Engineer
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System Design
•
hard
Design a machine learning deployment platform that can operate in strictly air-gapped environments with no internet access.
#Deployment
#Security
#Infrastructure
Machine Learning Engineer
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System Design
•
medium
Design a feature store capable of serving both high-throughput batch training and low-latency real-time inference.
#Databases
#Data Pipelines
#MLOps
Machine Learning Engineer
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System Design
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hard
Design a scalable system to ingest, process, and run computer vision models on daily satellite imagery updates for global supply chain monitoring.
#Computer Vision
#Distributed Systems
#Batch Processing
Machine Learning Engineer
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System Design
•
hard
Design a Retrieval-Augmented Generation (RAG) system over a corpus of highly classified documents, where users have different row-level access permissions.
#NLP
#Security
#Search
Machine Learning Engineer
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System Design
•
medium
Design an alerting system that monitors thousands of predictive maintenance models and notifies engineers when equipment failure probability exceeds a threshold.
#Monitoring
#Event-Driven Architecture
#Scalability
Machine Learning Engineer
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System Design
•
hard
Design a distributed graph processing pipeline to continuously update entity resolution models as new intelligence data arrives.
#Graph Databases
#Entity Resolution
#Data Pipelines
Machine Learning Engineer
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System Design
•
medium
Design a system to track data provenance and model lineage so that if a data source is corrupted, we can identify all affected downstream ML models.
#Data Governance
#DAGs
#Metadata Management
Machine Learning Engineer
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Technical
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medium
How do you handle severe class imbalance when training a model to detect rare adversarial events in a network?
#Classification
#Data Imbalance
#Evaluation Metrics
Machine Learning Engineer
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Technical
•
hard
Explain how you would optimize a large language model or vision model for inference latency on edge devices with limited compute.
#Model Optimization
#Edge AI
#Deep Learning
Machine Learning Engineer
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Technical
•
medium
In a production environment, how do you detect concept drift versus data drift, and how do you automate the retraining pipeline?
#Monitoring
#Data Drift
#CI/CD for ML
Machine Learning Engineer
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Technical
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easy
What are the trade-offs between using a Gradient Boosting Machine (like XGBoost) versus a Deep Neural Network for structured, tabular data?
#Tabular Data
#Trees
#Deep Learning
Machine Learning Engineer
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Technical
•
medium
Describe how you would implement an active learning strategy to minimize the manual labeling effort required from subject matter experts.
#Active Learning
#Human-in-the-loop
#Data Labeling
Machine Learning Engineer
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Technical
•
hard
How do you evaluate the performance of an unsupervised anomaly detection model when you have no ground truth labels?
#Unsupervised Learning
#Anomaly Detection
#Evaluation
Machine Learning Engineer
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Technical
•
medium
Explain the mathematical intuition behind the attention mechanism in Transformers and discuss its computational complexity.
#Transformers
#NLP
#Math
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.