Databricks Machine Learning Engineer Interview Questions + Guide in 2025

Databricks is a leading data and AI company that empowers organizations to unify and democratize data, analytics, and AI across their operations.

In the role of a Machine Learning Engineer at Databricks, you will be at the forefront of developing and optimizing machine learning models that drive the company's innovative AI solutions. Your key responsibilities will include exploring and analyzing performance bottlenecks in ML training and inference, designing and implementing libraries to overcome these challenges, and building tools for performance profiling and analysis. This role demands a strong foundation in deep learning frameworks like PyTorch and TensorFlow, as well as experience with high-performance linear algebra libraries and compiler technologies relevant to machine learning.

The ideal candidate will possess hands-on experience in writing CUDA code and have a deep understanding of GPU internals. Additionally, familiarity with distributed systems development and a track record of publications in reputable ML conferences can set you apart. Databricks values candidates who are not only technically skilled but also curious and adaptable, eager to learn new technologies and contribute to the company's mission of solving the world's toughest problems through advanced data and AI capabilities.

This guide will help you prepare for your interview by providing insights into the expectations and competencies required for the Machine Learning Engineer role at Databricks, allowing you to present yourself as a strong candidate.

The interview process for a Machine Learning Engineer at Databricks is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the dynamic environment of the company. The process typically consists of several key stages:

1. Initial Recruiter Screen

The first step involves a phone call with a recruiter, lasting about 30 minutes. This conversation is generally informal and focuses on your background, previous work experiences, and motivations for applying to Databricks. The recruiter will also gauge your fit within the company culture and discuss the role's expectations.

2. Technical Screening

Following the recruiter screen, candidates usually undergo one or two technical phone interviews. These sessions typically last around an hour and focus on coding challenges, often derived from platforms like LeetCode. Expect to solve problems related to data structures, algorithms, and possibly some machine learning concepts. Interviewers may also ask about your past projects and experiences, so be prepared to discuss them in detail.

3. Online Assessment

In some cases, candidates may be required to complete an online assessment that tests their coding skills and understanding of machine learning principles. This assessment usually consists of multiple questions, including algorithmic challenges and SQL queries, and is designed to evaluate your problem-solving abilities under time constraints.

4. Onsite Interviews

Candidates who pass the previous stages are invited to a virtual onsite interview, which can last several hours and typically includes multiple rounds. These rounds may consist of: - Technical Interviews: Focused on coding, system design, and machine learning concepts. You may be asked to solve complex problems, optimize algorithms, or design systems that leverage machine learning. - Behavioral Interviews: These sessions assess your soft skills, teamwork, and cultural fit within Databricks. Expect questions about your approach to collaboration, conflict resolution, and leadership experiences. - Managerial Round: A final interview with a hiring manager to discuss your career goals, expectations, and how you can contribute to the team.

5. Feedback and Decision

After the onsite interviews, candidates typically receive feedback within a few days. The decision-making process may involve discussions among interviewers to evaluate your performance across all rounds. If successful, you will receive an offer, which may include details about compensation, benefits, and other relevant information.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage.

Here are some tips to help you excel in your interview.

Understand the Technical Landscape

As a Machine Learning Engineer at Databricks, you will be expected to have a deep understanding of machine learning frameworks like PyTorch and TensorFlow, as well as experience with high-performance libraries such as cuDNN and MKL. Brush up on these technologies and be prepared to discuss your hands-on experience with them. Familiarize yourself with the latest trends in AI and machine learning, especially those relevant to Databricks' focus on generative AI and large-scale distributed systems.

Prepare for Coding Challenges

Expect to face rigorous coding challenges during your interviews. Many candidates reported that the technical interviews included LeetCode-style questions that tested data structures and algorithms. Practice solving medium to hard-level problems, particularly those involving graph algorithms, dynamic programming, and system design. Make sure you can articulate your thought process clearly while coding, as interviewers appreciate candidates who can explain their reasoning.

Showcase Your Projects

Be ready to discuss your past projects in detail. Interviewers are interested in understanding your role, the challenges you faced, and how you overcame them. Highlight any experience you have with performance profiling, optimization techniques, or building tools for machine learning. If you have contributed to open-source projects or published research, be sure to mention these as they can set you apart from other candidates.

Emphasize Collaboration and Communication Skills

Databricks values candidates who can work well in cross-functional teams. Be prepared to discuss how you have collaborated with product managers, engineers, and researchers in the past. Effective communication is key, especially when explaining complex technical concepts to non-technical stakeholders. Practice articulating your ideas clearly and concisely.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit within the company culture. Databricks emphasizes curiosity and a willingness to learn. Prepare to discuss your motivations for wanting to join Databricks, how you handle challenges, and your approach to teamwork. Use the STAR (Situation, Task, Action, Result) method to structure your responses.

Stay Informed About Databricks

Research Databricks' recent developments, especially in the realm of generative AI and machine learning. Understanding the company's mission and how your role contributes to it will help you align your answers with their goals. This knowledge will also demonstrate your genuine interest in the company.

Follow Up Professionally

After your interviews, send a thank-you email to your interviewers. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This small gesture can leave a positive impression and keep you top of mind as they make their decision.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Databricks. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Databricks. The interview process will likely assess your technical skills in machine learning, coding, system design, and your ability to communicate complex ideas effectively. Be prepared to discuss your past projects, demonstrate your problem-solving skills, and showcase your understanding of machine learning frameworks and algorithms.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key characteristics of both supervised and unsupervised learning, including how they are used in practice. Mention specific algorithms that fall under each category.

Example

“Supervised learning involves training a model on labeled data, where the input-output pairs are known, such as in regression and classification tasks. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like clustering algorithms.”

2. Describe a project where you implemented a machine learning model. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project scope, the model you chose, and the challenges you encountered, such as data quality issues or model performance.

Example

“In a recent project, I developed a predictive model for customer churn. One challenge was dealing with imbalanced data, which I addressed by using SMOTE for oversampling the minority class. This improved the model's accuracy significantly.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization techniques.

How to Answer

Discuss various techniques to prevent overfitting, such as cross-validation, regularization, and pruning.

Example

“To combat overfitting, I typically use techniques like L1 and L2 regularization to penalize large coefficients. Additionally, I implement cross-validation to ensure the model generalizes well to unseen data.”

4. What is hyperparameter tuning, and how do you approach it?

This question evaluates your knowledge of model optimization.

How to Answer

Explain what hyperparameters are and describe methods for tuning them, such as grid search or random search.

Example

“Hyperparameter tuning involves optimizing the parameters that govern the training process, such as learning rate and batch size. I often use grid search combined with cross-validation to find the best set of hyperparameters for my models.”

Coding and Algorithms

1. Can you write a function to implement a binary search algorithm?

This question assesses your coding skills and understanding of algorithms.

How to Answer

Be prepared to write clean, efficient code and explain your thought process as you go.

Example

“I would implement a binary search function that takes a sorted array and a target value, returning the index of the target if found. The function would repeatedly divide the search interval in half until the target is located or the interval is empty.”

2. How would you optimize a Spark job?

This question tests your knowledge of distributed computing and performance optimization.

How to Answer

Discuss techniques for optimizing Spark jobs, such as data partitioning, caching, and using the appropriate data formats.

Example

“To optimize a Spark job, I would ensure that data is properly partitioned to minimize shuffling. Additionally, I would use caching for frequently accessed data and choose efficient data formats like Parquet for better performance.”

3. Explain the concept of a hash table and its applications.

This question evaluates your understanding of data structures.

How to Answer

Define a hash table and discuss its time complexity for various operations, along with real-world applications.

Example

“A hash table is a data structure that maps keys to values for efficient data retrieval. It offers average-case O(1) time complexity for insertions, deletions, and lookups. Common applications include implementing associative arrays and database indexing.”

4. Describe how you would implement a load balancing algorithm.

This question assesses your knowledge of system design and distributed systems.

How to Answer

Explain the principles of load balancing and discuss different algorithms, such as round-robin or least connections.

Example

“I would implement a round-robin load balancing algorithm, where requests are distributed evenly across servers in a cyclic manner. This ensures that no single server is overwhelmed, improving overall system performance.”

Statistics and Probability

1. What is the Central Limit Theorem, and why is it important?

This question tests your understanding of statistical concepts.

How to Answer

Explain the theorem and its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for hypothesis testing and confidence interval estimation.”

2. How do you assess the performance of a machine learning model?

This question evaluates your knowledge of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and when to use them.

Example

“I assess model performance using metrics like accuracy for balanced datasets, while precision and recall are more informative for imbalanced datasets. The F1 score provides a balance between precision and recall, and ROC-AUC helps evaluate the model's ability to distinguish between classes.”

3. Explain the concept of p-values in hypothesis testing.

This question tests your understanding of statistical significance.

How to Answer

Define p-values and discuss their role in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating statistical significance.”

4. What is regularization, and why is it used in machine learning?

This question assesses your understanding of model complexity and generalization.

How to Answer

Explain regularization techniques and their purpose in preventing overfitting.

Example

“Regularization adds a penalty to the loss function to discourage overly complex models. Techniques like L1 (Lasso) and L2 (Ridge) regularization help maintain model simplicity while improving generalization to unseen data.”