Databricks
Unified analytics platform built on Apache Spark for data engineering and ML.
4 Rounds
~21 Days
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
Databricks heavily values 'Truth-seeking'. Tell me about a time when you had to challenge a deeply held assumption in your team's ML architecture or model choice. How did you prove your case?
#Core Values
#Communication
#Data-Driven Decisions
Machine Learning Engineer
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Behavioral
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hard
Tell me about a time you had to dive deep into a complex distributed system bug that was silently degrading your machine learning model's performance in production.
#Debugging
#Production ML
#Problem Solving
Machine Learning Engineer
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Coding
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medium
Implement a LazyArray class in Python that takes an array of integers. It should support two operations: map(function) which applies a function to all elements, and indexOf(value) which returns the index of the first occurrence of the value. The map operation must be lazy (deferred execution) and optimized so that indexOf does not compute unnecessary elements.
#Object-Oriented Design
#Lazy Evaluation
#Arrays
Machine Learning Engineer
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Coding
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hard
Given a list of tasks with dependencies (represented as a directed graph) and the execution time for each task, write a function to calculate the minimum time required to complete all tasks assuming you have infinite parallel workers.
#Graphs
#Topological Sort
#Dynamic Programming
Machine Learning Engineer
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Coding
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medium
Given two sparse matrices A and B represented as lists of non-zero elements (row, col, value), write a function to compute their product. How would you optimize this for a distributed environment?
#Math
#Hash Maps
#Distributed Computing
Machine Learning Engineer
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Coding
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medium
Given a stream of user activity logs (timestamp, user_id, action), write a function to find the longest continuous session for each user. A session ends if there is a gap of more than 30 minutes between actions.
#Sliding Window
#Hash Maps
#Sorting
Machine Learning Engineer
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Coding
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medium
You are given a list of intervals representing compute jobs on a cluster [start, end] and an associated CPU core requirement for each job. Write a function to determine the maximum number of CPU cores used at any point in time.
#Sweep Line
#Intervals
#Sorting
Machine Learning Engineer
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Coding
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medium
Implement a thread-safe Rate Limiter class for an API. It should support a method `is_allowed(client_id)` which returns True if the client has made fewer than N requests in the last M seconds, and False otherwise.
#Concurrency
#System Design
#Queues
Machine Learning Engineer
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System Design
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hard
Design a scalable LLM serving architecture for a multi-tenant environment. How would you handle thousands of users requesting inference from different fine-tuned versions of a base model like Llama-3?
#LLMs
#Multi-tenancy
#GPU Optimization
#Model Serving
Machine Learning Engineer
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System Design
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hard
Design a machine learning system to predict job/cluster failures in a distributed computing environment like Databricks. How do you handle the massive volume of telemetry data and the extreme class imbalance?
#Predictive Maintenance
#Streaming Data
#Imbalanced Data
Machine Learning Engineer
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System Design
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medium
Design a model registry and experiment tracking system similar to MLflow. How do you handle model versioning, lineage tracking, and concurrent writes from thousands of distributed training runs?
#MLOps
#Databases
#API Design
Machine Learning Engineer
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System Design
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medium
Design an automated hyperparameter tuning service that can schedule and manage thousands of concurrent ML jobs. How do you allocate resources and handle early stopping for poorly performing runs?
#AutoML
#Resource Management
#Scheduling
Machine Learning Engineer
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Technical
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medium
How would you implement a distributed K-Means clustering algorithm from scratch using Spark RDDs or a MapReduce paradigm?
#Distributed Computing
#Apache Spark
#Clustering
Machine Learning Engineer
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Technical
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hard
Explain the differences between Data Parallelism, Tensor Parallelism, and Pipeline Parallelism. In what scenarios would you choose one over the others when training a 70B parameter model?
#Deep Learning
#Distributed Training
#LLMs
Machine Learning Engineer
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Technical
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medium
What are the primary bottlenecks when using Stochastic Gradient Descent (SGD) in a distributed cluster? How do algorithms like Ring-AllReduce mitigate these bottlenecks?
#Optimization Algorithms
#Networking
#Distributed Systems
Difficulty Radar
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.