Deliveroo Machine Learning Engineer Interview Questions + Guide in 2025

Deliveroo is a leading online food delivery service that connects consumers with their favorite local restaurants, focusing on enhancing customer experience through technology and data-driven solutions.

As a Machine Learning Engineer at Deliveroo, you will be responsible for developing and deploying machine learning models that enhance the platform's efficiency and user experience. Key responsibilities include collaborating with data scientists and product managers to design and implement data pipelines, creating predictive models to optimize delivery routes, and improving customer recommendations based on behavioral data. A strong understanding of algorithms, statistical modeling, and programming languages such as Python or R is essential, along with experience in cloud platforms like AWS or GCP.

The ideal candidate will possess a deep passion for machine learning and a proven track record of applying theoretical knowledge to practical problems. You should be comfortable working in a fast-paced environment where business impact is prioritized, and able to communicate complex concepts clearly to non-technical stakeholders. Deliveroo values innovation, collaboration, and a customer-centric approach, so a focus on how your solutions can benefit users will set you apart in the interview process.

This guide will help you prepare for your interview by providing insights into the specific skills and experiences that Deliveroo values, as well as the types of questions you may encounter during the interview process.

The interview process for a Machine Learning Engineer at Deliveroo is structured and thorough, designed to assess both technical skills and cultural fit. Here’s a breakdown of the typical stages you can expect:

1. Initial Screening

The process begins with a brief phone call with a recruiter. This initial screening lasts about 20-30 minutes and focuses on your background, experience, and motivation for applying to Deliveroo. The recruiter will also provide an overview of the interview process and what to expect in subsequent rounds.

2. Technical Screening

Following the initial screening, candidates typically undergo a technical interview, which may be conducted through a third-party service like Karat. This session usually lasts around 60 minutes and includes a mix of theoretical questions and coding exercises. You may be asked to solve algorithmic problems and demonstrate your understanding of data structures and machine learning concepts.

3. Take-Home Assignment

If you pass the technical screening, you will be given a take-home assignment. This task is designed to evaluate your practical skills in machine learning, including exploratory data analysis, preprocessing, modeling, and evaluation. Candidates are generally expected to complete this assignment within a specified timeframe, often around 48 hours. Be prepared to present your findings and methodology in a subsequent interview.

4. Final Interview Rounds

The final stage consists of multiple interviews, typically spread over one or two days. These interviews may include: - Machine Learning Theory: Expect questions covering fundamental concepts such as gradient descent, backpropagation, and various regression techniques. - Machine Learning Practical: You may be presented with a real-world problem relevant to Deliveroo and asked to discuss your approach to solving it. - Behavioral Interview: This round often employs the STAR method (Situation, Task, Action, Result) to assess how you handle various workplace scenarios and challenges. - Business Acumen: Interviewers will be interested in your understanding of how machine learning can solve business problems, so be prepared to discuss metrics, causal inference, and experiment design.

Throughout the process, candidates should be ready for a mix of technical and behavioral questions, as well as case studies that require analytical thinking and problem-solving skills.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

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

Emphasize Practical Application Over Memorization

During your interviews, focus on demonstrating how you can apply machine learning concepts to real-world problems rather than just reciting equations or theoretical knowledge. Interviewers at Deliveroo seem to value practical insights and business impact, so be prepared to discuss how your work can drive results and improve processes within the company.

Prepare for a Multi-Stage Process

Expect a lengthy interview process that may span several weeks. Familiarize yourself with the structure, which typically includes an initial HR screening, technical interviews, a take-home assignment, and multiple rounds of interviews with team members. Being organized and prepared for each stage will help you manage your time effectively and reduce stress.

Master the Take-Home Assignment

The take-home task is a critical component of the interview process. While the guidelines suggest a limited time commitment, many candidates report spending significantly longer to produce quality work. Aim to balance thoroughness with efficiency; consider setting a strict time limit for yourself to avoid burnout. Make sure to clearly present your findings and be ready to discuss your approach during the follow-up interviews.

Brush Up on Causal Inference and Experiment Design

Given the emphasis on causal inference and analytical problem-solving in the interview process, ensure you are well-versed in these areas. Be prepared to discuss how you would design experiments, interpret data, and draw conclusions that can inform business decisions. Familiarize yourself with common metrics and evaluation techniques relevant to Deliveroo's operations.

Engage in Behavioral Questions

Behavioral questions are a significant part of the interview process. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences and be ready to discuss how you’ve handled challenges, influenced others, and contributed to team success. This will help you demonstrate your fit within the company culture.

Be Ready for Technical Challenges

Technical interviews may include coding exercises and system design questions. Brush up on your coding skills, particularly in languages relevant to the role, and be prepared to solve problems on the spot. Familiarize yourself with common algorithms and data structures, as well as machine learning concepts like gradient descent, backpropagation, and model evaluation.

Show Enthusiasm for the Company and Role

Deliveroo values candidates who are genuinely interested in the company and its mission. Be prepared to articulate why you want to work there and how your skills align with their goals. Research recent developments in the company and be ready to discuss how you can contribute to their success.

Maintain Professionalism Throughout

While some candidates have reported unprofessional experiences during the interview process, it’s essential to remain courteous and professional at all times. Regardless of the interviewers' demeanor, focus on showcasing your skills and maintaining a positive attitude. This will leave a lasting impression and reflect well on your character.

By following these tailored tips, you can enhance your chances of success in the interview process at Deliveroo for the Machine Learning Engineer role. Good luck!

Machine Learning Concepts

1. Explain how you calculate backpropagation in neural networks.

Understanding backpropagation is crucial for any machine learning engineer, as it is the backbone of training neural networks.

How to Answer

Discuss the concept of gradient descent and how it is used to minimize the loss function by adjusting weights in the network. Be sure to mention the chain rule and how it applies to the calculation of gradients.

Example

"Backpropagation involves calculating the gradient of the loss function with respect to each weight by applying the chain rule. This allows us to update the weights in the direction that reduces the loss, effectively training the neural network. The process starts from the output layer and moves backward through the network, adjusting weights based on the error at each layer."

2. What is gradient descent, and how does it work?

This question tests your understanding of optimization techniques used in machine learning.

How to Answer

Explain the concept of gradient descent as an iterative optimization algorithm used to minimize a function by moving in the direction of the steepest descent.

Example

"Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models. It works by calculating the gradient of the loss function with respect to the model parameters and updating the parameters in the opposite direction of the gradient. This process is repeated until convergence is achieved."

3. Describe the difference between Lasso and Ridge regression. When would you use one over the other?

This question assesses your knowledge of regularization techniques in regression analysis.

How to Answer

Discuss the key differences in how Lasso and Ridge regression penalize coefficients and the scenarios in which each is preferable.

Example

"Lasso regression adds an L1 penalty to the loss function, which can lead to sparse solutions by forcing some coefficients to zero. This is useful when we suspect that many features are irrelevant. Ridge regression, on the other hand, adds an L2 penalty, which shrinks coefficients but does not eliminate them. I would use Lasso when feature selection is important and Ridge when I want to retain all features but control for multicollinearity."

4. Can you explain the concept of overfitting and how to prevent it?

Overfitting is a common issue in machine learning, and understanding it is essential for model performance.

How to Answer

Define overfitting and discuss various techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

"Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, resulting in poor generalization to new data. To prevent overfitting, I use techniques like cross-validation to ensure the model performs well on unseen data, apply regularization methods to penalize overly complex models, and utilize techniques like dropout in neural networks."

Statistics and Probability

1. How would you estimate the delivery rate for a service like Deliveroo?

This question tests your analytical thinking and ability to apply statistical methods to real-world scenarios.

How to Answer

Outline a structured approach to estimating the delivery rate, including data collection, analysis, and modeling.

Example

"To estimate the delivery rate for Deliveroo, I would first gather historical data on delivery times, distances, and order volumes. I would then analyze this data to identify patterns and factors affecting delivery rates, such as time of day and location. Finally, I would build a predictive model using regression analysis to estimate future delivery rates based on these factors."

2. Explain A/B testing and how you would implement it in a business context.

A/B testing is a fundamental concept in data-driven decision-making.

How to Answer

Discuss the design of an A/B test, including hypothesis formulation, sample size determination, and analysis of results.

Example

"A/B testing involves comparing two versions of a variable to determine which one performs better. In a business context, I would start by formulating a hypothesis, such as 'changing the color of the order button will increase click-through rates.' I would then randomly assign users to either the control group (original button) or the treatment group (new button) and measure the outcomes. After collecting sufficient data, I would analyze the results using statistical tests to determine if the observed differences are significant."

3. What metrics would you use to evaluate the performance of a machine learning model?

This question assesses your understanding of model evaluation.

How to Answer

Discuss various metrics relevant to the type of model being evaluated, such as accuracy, precision, recall, F1 score, and AUC-ROC.

Example

"To evaluate the performance of a machine learning model, I would consider metrics such as accuracy for overall performance, precision and recall for classification tasks, and the F1 score for a balance between precision and recall. For binary classification problems, I would also look at the AUC-ROC curve to assess the model's ability to distinguish between classes."

4. Describe a time when you had to analyze a large dataset. What tools did you use?

This question evaluates your practical experience with data analysis.

How to Answer

Provide a specific example of a project where you analyzed a large dataset, including the tools and techniques you employed.

Example

"In my previous role, I analyzed a large dataset containing customer transaction records. I used Python with libraries like Pandas for data manipulation and Matplotlib for visualization. By cleaning the data and performing exploratory data analysis, I was able to uncover insights that informed our marketing strategy, leading to a 15% increase in customer engagement."

Behavioral Questions

1. Tell me about a time when you had to persuade someone that your approach was the right one.

This question assesses your communication and persuasion skills.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

"In a previous project, I proposed a new machine learning model to improve our recommendation system. The team was initially hesitant due to the complexity of the model. I organized a presentation to explain the benefits and backed it up with data from a pilot test. Ultimately, my approach was adopted, resulting in a 20% increase in user engagement."

2. How do you deal with conflicts in your team?

This question evaluates your conflict resolution skills.

How to Answer

Discuss your approach to resolving conflicts, emphasizing communication and collaboration.

Example

"When conflicts arise in my team, I prioritize open communication. I encourage team members to express their viewpoints and facilitate a discussion to understand each perspective. By focusing on common goals and finding a compromise, we can often resolve conflicts amicably and strengthen our collaboration."

3. Describe a project you’ve been working on that was ambiguous. How did you handle it?

This question assesses your problem-solving skills in uncertain situations.

How to Answer

Explain how you approached the ambiguity, including any frameworks or methodologies you used.

Example

"I once worked on a project where the requirements were not clearly defined. To handle the ambiguity, I initiated a series of meetings with stakeholders to gather their expectations and clarify objectives. I also created a project roadmap with milestones to ensure we stayed on track. This proactive approach helped us navigate the uncertainty and deliver a successful outcome."

4. Tell me about a time where you faced conflicting perspectives on an issue.

This question evaluates your ability to navigate differing opinions.

How to Answer

Use the STAR method to describe the situation and how you managed the conflict.

Example

"During a project, two team members had conflicting views on the best algorithm to use. I facilitated a meeting where each could present their case, supported by data. After discussing the pros and cons, we decided to run a comparative analysis of both algorithms. This approach not only resolved the conflict but also led to a more informed decision."