Apple Machine Learning Engineer Interview Prep

Machine Learning Engineers play a crucial role in today’s data-driven world. They help to design and develop algorithms that can automate data-driven decision-making. Artificial Intelligence and ML systems automatically learn from data patterns and make decisions, eliminating errors arising from human involvement.

Apple actively hires Machine Learning Engineers to help automate deep learning data models and build scalable ML systems and applications that understand consumer behavior by analyzing huge swathes of data from user activity.

Apple pays its Machine Learning Engineers highly rewarding salaries, prompting interest from engineers from across the globe.

If you are getting ready for your Machine Learning Engineer interview at Apple, check out our technical interview checklist, interview questions page, and salary negotiation e-book to get interview-ready!

Also, readApple Interview Questions and The Ultimate Guide To Crack Apple’s On-site Interview for specific insights and guidance on Apple tech interviews.

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In this piece, we’ll understand the Apple Machine Learning interview process, the type of questions to expect, what responsibilities your role will entail, and some noteworthy tips to ace your machine learning interview.

Here’s what we’ll cover:

• The Role of Machine Learning Engineers at Apple
• Apple Machine Learning Engineer Interview — Skills and Qualifications
• Machine Learning Engineering Teams at Apple
• Apple Machine Learning Engineer Interview Process
• Apple Machine Learning Engineer Interview Questions
• Apple Machine Learning Engineer Interview Prep Tips

The Role of Machine Learning Engineers at Apple

Being one of the largest tech companies in the world, Apple deals with enormous chunks of user data that need to be studied and analyzed. Scalable ML systems that work optimally make small work of the enormity of data and automate data-driven decision-making to enhance user experience and engagement.

To build and maintain these systems, machine learning engineers at Apple are required to:

• Work cross-functionally with data and software teams to ensure seamless product delivery
• Design the architecture for ML systems, understand core dependencies and implement solutions
• Design and develop comprehensive algorithms to automate deep learning models
• Design the architecture for internal AI systems
• Build scalable models that can automate decision making to enhance user experience
• Build AI and ML tools to monitor and enhance the performance of Apple’s innovative products

Apple Machine Learning Engineer Interview — Skills and Qualifications

Below are the qualifications required to apply for a Machine Learning Engineer role at Apple:

• A degree in Computer Science, or IT, preferably a Master’s Degree
• Working knowledge of algorithms and deep learning data models
• Working knowledge of data processing technologies and Machine Learning frameworks
• Working knowledge of predictive data models and automating predictive models
• Proven experience in building system applications and scalable ML systems
• Working knowledge of an Object-Oriented Programming Language
• 4+ years of experience in the field

Machine Learning Engineering Teams at Apple

Apple has multiple teams that employ the services of Machine Learning and AI engineers. As an ML engineer, you would be directly involved in integrating innovative experiences into Apple products by being part of one of the following teams:

The Machine Learning Infrastructure Team

The ML Infrastructure Team is responsible for building the foundation for Apple’s innovative products.

As part of this team, you’ll get to work with a wide variety of analytics and design tools to integrate ML with hardware, software, and deep learning models. The areas of work will include Data Engineering, Systems Engineering, Back-end Engineering, and Data Science.

The Deep Learning and Reinforcement Learning Team

As part of this team, you’ll be working to solve large-scale, real-world problems with data scientists.

You’ll also be closely involved in developing complex learning models, including deep learning models, generative models, deep reinforcement learning, multimodal input streams, inverse, and deep reinforcement learning, and game theory.

Natural Language Processing Team

As a member of this team, you’ll be involved in tackling user-related challenges through NLP. Your areas of work will include text-to-speech engineering, language modeling, integrating speech frameworks in Apple’s products (Siri), data science, and language modeling.

The Applied Research Team

As part of this team, you’ll be directly involved with Machine Learning research of algorithms and their integration into Apple’s systems, products, and applications. The main areas of work in this team will be Machine Learning Platform Learning, Applied Data Science, and Systems Engineering.

The Computer Vision Team

The Computer Vision Team is a Multidisciplinary team that mainly focuses on designing and developing algorithms for the analysis of sensor data streams. As part of this team, you’ll be involved in building algorithms for image processing and neural networks.

As part of this team, the main areas of work include data science, deep learning, and computer vision.

Apple Machine Learning Engineer Interview Process

The interview process for ML engineer roles at Apple consists of three rounds

• The Initial Screen: A recruiter from Apple will get in touch with you to understand your skills, compensation, level of experience, and what your expectations are from the role. You’ll be contacted by the recruiter either through LinkedIn or directly if you’ve applied through Apple’s careers page.
• The Technical Screen: This remote interview round predominantly tests your ability to solve coding problems, mostly around algorithms. A hiring manager conducts this round to understand your prowess with programming and your ability to display sound analytical abilities in solving problems. This round either takes place via a shared doc or an interviewing tool. Apart from the coding element, you can expect a few questions around ML and AI as well.
• The On-site: The on-site interview evaluates your coding skills, ML knowledge, and behavioral tendencies. If the role you’re applying to involves product design, you can expect a dedicated Product Design round in the on-site. Each of these rounds is conducted by one or many hiring managers. The rounds are usually time-bound, lasting about 30-40 minutes each.

Related Read:
Google Machine Learning Engineer Interview Process
Amazon Machine Learning Engineer Interview Process

Apple Machine Learning Engineer Interview Questions

Interview questions that appear in Apple’s Machine Learning interview fall under the following categories:

• Coding interview questions
• Machine Learning interview questions
• Behavioral interview questions

Let’s look at the topics to prepare and sample interview questions in each of these areas.

1. Coding Interview Questions

Coding is an extremely important skill for ML engineering roles. Below are the topics to prepare for the coding aspect of ML engineer interviews at Apple:

• Recursion
• Trees and Graphs
• Dynamic Programming
• Hash Tables and Queues
• Arrays, Strings, Linked Lists
• Graph Algorithms and Greedy Algorithms
• Sorting Algorithms — Quicksort, Merge Sort, Heap Sort, etc.

Sample Coding Interview Questions

1. You’re given an array of integers. Write a code to determine if there are three integers in the array whose sum equals a given value.
2. Write a program function to clone a given directed graph such that the cloned graph has the same edges and vertices.
3. You’re given two sorted Linked Lists. Write a code to merge the linked lists such that the returning linked list is also sorted.
4. You’re provided with the roots of two Binary Trees. Write a code to determine if the two Binary Trees are identical or not.
5. Given the root node of a Binary Tree “B,” write a code to swap the right and left children for each node of the tree.
6. Write a code to reverse the order of words in a given sentence.
7. You’re given an array of size N. Write a code to search for the second largest element in the array.
8. You’re given an array A of size N. Write a function to find the minimum index-based distance between two elements in the array.

For more coding problems with complete solutions, visit our Problems Page

2. Machine Learning Interview Questions

ML interview questions are asked during the Technical Phone Screen Round and during the On-site ML round. Here are some sample ML interview questions asked at the Apple ML Engineering interview. Before that, let’s look at the topics to prepare:

• BigQuery
• TensorFlow
• Cloud Vision
• Model Validation
• Model Optimization
• Deep Learning Models
• Predictive Models
• Data Processing
• ML and DL frameworks
• Amazon ML engine

Sample Machine Learning Interview Questions

1. Explain the different types of Machine Learning algorithms
2. Differentiate between supervised and unsupervised learning with relevant examples
3. In a Naive Bayes, what is the term Naive?
4. Explain Principal Component Analysis and its applications
5. Explain in detail the working of the SVL algorithm

3. Behavioral Interview Questions

Behavioral interviews take place during the on-site interview. They are an important part of the decision-making process. Below are some behavioral interview questions asked at Apple’s ML engineer interview:

1. Tell us about a time when you worked on a highly demanding project that stretched you
2. Tell us about the most important ML project you worked on in the past
3. What have been your biggest challenges as an ML engineer?
4. How do you ensure to maintain your mental health?
5. Tell us about a time when you disagreed with a superior.
6. Tell us about a time when you had to adapt quickly and pick up new skills for a project.

Recommended Reading: Behavioral Interview Questions for Software Developers

Apple Machine Learning Engineer Interview Prep Tips

At interview Kickstart, we’ve trained thousands of engineers for technical interviews at top tech companies. Having understood what it takes to crack these interviews, we’ve compiled a list of tips to help you ace your upcoming ML Engineer Interview:

1. Begin your preparation at least 10 weeks before your interview. Spending 10-12 weeks will help you cover technical concepts adequately. Remember, the concepts to cover are vast, and the interview has almost nothing to do with your experience level. Meaning that even if you have good domain experience, you cannot see yourself coming through if you’re found lacking on other fronts.
2. Solve coding problems every day. Make sure you solve at least one problem a day to brush up on important algorithms and data structures concepts. Identify patterns while solving problems and use power patterns while solving new problems.
3. Practice Mock Interviews with experienced professionals. The power of mock interviews is often underestimated when it comes to interviewing at big tech companies. By practicing mocks with expert instructors, you can refine your interviewing skills, overcome interview anxiety, and tide over your weak areas.
4. Prepare for behavioral interviews. Don’t ignore behavioral interviews. They’re a crucial part of the hiring process. Being unprepared can cost you the offer.
5. Create a strong project portfolio: A good project portfolio will apprise interviewers of the level of skill or experience you’ve gained over the course of your professional experience. While creating your project portfolio, make sure to list your projects in the STAR format.
6. Write a follow-up mail after the interview. Thank the recruiters for their time and for giving you the opportunity. It makes a good impression.

If you’re looking for a structured interview preparation plan and guidance from industry experts, enroll for Interview Kickstart’s Machine Learning Interview Course. It is the first-of-its-kind interview prep course designed specifically to help ML engineers crack the toughest tech interviews at FAANG+ companies. Click here to learn more.

Get Set for Your Next Machine Learning Engineer Interview

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