ML Engineer Interview

This is the initial step of any interview process—the resume screening round, where recruiters carefully evaluate your resume to determine if your experience aligns with the requirements of the position. Understandably, this step is highly competitive, with numerous candidates vying for a limited number of spots.

If you successfully advance past this stage, companies like Meta and others typically offer valuable resources through their recruiting portal to assist you in your upcoming interview preparation.

During the initial screening round of the ML Engineer interview process, the recruiter will likely ask typical behavioral and resume questions such as "Tell me about yourself" or "why our company?". 

Understandably, their goal is to assess your qualifications and determine if you are a good fit for the role. In fact, they may also inquire about your research background in ML, as this field often values research experience. 

Therefore, if you have contributed to research papers, be prepared to discuss your findings and provide examples.

However, if you lack formal research experience, it is not necessarily a deal-breaker but you should still be prepared to explain why you have focused your energy in other areas and highlight any relevant projects or practical experience you have gained.

Here's a list of example questions that may be asked during the initial screening round for an ML Engineer position:

• Tell me about yourself and your background in machine learning.
• Why are you interested in this company and the ML Engineer role?
• Can you explain your current project and your contributions to it?
• What challenges have you faced in your previous ML projects, and how did you overcome them?
• Have you published any research papers in the field of machine learning? If so, could you discuss your findings and contributions?
• How do you stay updated with the latest developments and advancements in machine learning?
• Have you worked on any collaborative ML projects or interdisciplinary teams? How did you contribute to the team's success?

If you make it through the initial screening, the next step is a coding interview, which is largely a technical interview and the emphasis is on evaluating your intuition for ML theory, including basic concepts and algorithms. Therefore, it is essential to demonstrate your understanding of statistics, experimental models, and system design, as these aspects play a significant role in ML engineering.

This segment may also include a take-home assignment depending on the company's interview process. The aim is to assess your ability to apply ML concepts, write clean and efficient code, and deliver a functioning solution that solves a specific problem.

Examples of take-home coding assignments may include tasks like:

1. Implementing a machine learning algorithm, such as linear regression or k-means clustering, from scratch.
2. Developing a neural network model for a specific task, like image classification or natural language processing.
3. Building a recommendation system using collaborative filtering techniques.
4. Creating a data preprocessing pipeline to handle feature extraction and normalization.
5. Optimizing a model's performance through hyperparameter tuning and regularisation techniques.

The ML Engineer interview process typically encompasses a broad assessment to evaluate your intelligence, intuition, and overall fit for the company. However, the specific interview rounds can vary from one company to another.

For instance, Apple's interview process for MLE involves a phone screen followed by on-site (or Zoom) interviews, focusing heavily on your past projects and most sought-after Deep Learning techniques. On the other hand, Amazon's Machine Learning interview comprises behavioral, software engineering, and machine learning questions. At Facebook, you can expect a comprehensive evaluation, including coding interviews, system design interviews, machine learning design interviews, and behavioral interviews.

In general, an ML Engineering interview consists of four rounds:

1. Coding Interview
2. System Design Interview
3. Machine Learning Design Interview
4. Behavioral Interview

To enhance your chances of success, it is crucial to prepare for even the most challenging questions and remain composed. Let's dive into each of these rounds:

During the onsite ML Engineer interview, you can anticipate a mix of general coding questions that assess your coding skills, as well as questions on optimization, time complexity, and space complexity. The coding interview can be challenging, and in some cases, you may be given a home project assignment to complete. 

Here are some questions you can expect:

• What’s the difference between an array and a linked list?
• Suppose you have a matrix of numbers. How can you easily compute the sum of any rectangle (i.e. a range [row_start, row_end, col_start, col_end]) of those numbers? How would you code this?
• How to sort in O(Logn) time?
• Simple manipulation of arrays and map

To ace this round, 

• It is advisable to familiarize yourself with the types of questions you may encounter. You may be asked to tackle problems such as palindrome detection or string reversal. It is also not uncommon to encounter coding questions that involve recursion or require the implementation of algorithms like gradient descent. Therefore, make sure you are well-versed with such common coding challenges so as to avoid being caught off guard and be able to approach the problems with confidence.
• It's important to recognize that as a Machine Learning Engineer, your role leans more toward software engineering than data science. Therefore, use the coding interview to emphasize your ability to write efficient and well-structured code.

The system design round of the onsite ML Engineer interview is where you are typically challenged to create a comprehensive high-level design for a modern tech product, such as Instagram, Twitter,  and Facebook Messenger. 

You may be expected to:

• Define the objectives and goals of the system
• Identify the key components, data sets, and their interactions
• Touch upon scalability, performance, and fault-tolerance considerations
• Consider security and privacy concerns
• Consider trade-offs, consider various design options, and, communicate your ideas effectively.

Here are some questions you can expect:

• How would you design a recommendation system (like Amazon)?
• Design an API rate limiter.
• Design an ML labeling system.
• Explain how you would design a recommendation system for an e-commerce platform.
• Design a real-time chat application with millions of active users.

This interview basically aims to assess your problem-solving approach and your ability to create system designs that effectively cater to user needs. They are interested in understanding your thinking process when faced with open-ended problems and evaluating your skills as a valuable team member.

Therefore, your prep for this round should include the following:

• Gain knowledge of system design concepts- understand the architecture behind popular tech systems, and practice designing high-level systems with a focus on scalability and fault tolerance.
• Be ready to demonstrate your critical thinking skills, and problem-solving abilities, and consider every nitty-gritty of the design while taking into consideration input from your interviewers (ask clarifying questions where needed)
• Lastly, be articulate about your design choices and tradeoffs–-keep your interviewers in the loop by explaining your thought process every step of the way and soliciting their feedback on your design. 

During the Machine Learning Design Interview, you can expect questions that delve into your core understanding of ML algorithms and concepts. The purpose is to evaluate your knowledge and expertise across various domains of data science and machine learning and your ability to implement ML concepts effectively, encompassing programming, mathematics, statistics, and fundamental principles of machine learning.

The questions posed in this interview round will aim both to gauge your proficiency in translating theoretical ML concepts and ability to apply them to practical scenarios. You may be asked to discuss various feature engineering techniques, assess model performance, present solutions to address specific ML-related challenges or demonstrate your knowledge of data preprocessing and post-processing techniques.

Here are examples of ML design questions:

• What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?
• How do you prevent overfitting?
• What is K-means algorithm?
• What’s the difference between MLE and MAP inference?
• How do you approach data preprocessing and feature selection in your ML projects?

For you to excel this round, 

• it is important to display a deep understanding of ML algorithms, their mathematical underpinnings, and how they are applied to real-world problems.
• Additionally, being well-versed in programming languages commonly used in ML, such as Python, and frameworks like TensorFlow or PyTorch, will enhance your ability to articulate your ideas effectively.
• It's advisable to take the help of a mentor or mock interviewer to practice designing ML models and exploring their components, as well as diving into different ML use cases and communicating the trade-offs involved.

During the behavioral interview, you will encounter a bunch of hypothetical/scenario-based or specific questions related to your background, achievements, and reasons for applying to the company. You can also expect questions on fundamental ML concepts, your recent projects, and how you would approach solving specific ML/DL problems. The aim of this is to get insight into your professional experience, your contribution to your previous role, whether you align with the company's core values, and so on. They are also looking to assess your ability to articulate complex ideas in a clear and concise manner for non-technical individuals

Understandably, it is a good idea to prepare concise explanations, supported by examples within a context that your interviewer can relate to. For example, if asked to explain a complex ML concept, consider using real-world analogies or scenarios that can make the concept more understandable to someone without technical expertise. Or when asked about a recent project, describe it in a way that highlights your ML expertise, the problem you addressed, the methodology used, and the impact of your solution.

Here are some example questions you may encounter in this round:

• How would you explain machine learning to a kid?
• How do you stay up to date with the latest news and trends in machine learning?
• What are the last machine learning papers you’ve read?
• Can you describe a complex ML algorithm or model that you have implemented in the past? What was the outcome?