Nail Apple’s Machine Learning Engineer Interview — What and How to Prepare

It takes years of experience in software engineering and data science to become a machine learning (ML) engineer at Apple. With the rise in demand and popularity of ML engineer jobs and Apple’s reputation as a top-notch employer, machine learning jobs at Apple are difficult to crack but worth the effort, both in equal measure.

If you are preparing for a machine learning engineer role, check out our technical interview checklist, interview questions page, and salary negotiation e-book to get interview-ready! Also, read Google Machine Learning Engineer Interview Process and Amazon Machine Learning Engineer Interview Process for specific insights and guidance on machine learning interviews at FAANG. 

Having trained over 6,000 software engineers, software developers, and engineering managers, we know what it takes to crack the toughest tech interviews. Since 2014, Interview Kickstart alums have been landing lucrative offers from FAANG and Tier-1 tech companies, with an average salary hike of 49%. The highest ever offer received by an IK alum is a whopping $933,000! 

At IK, you get the unique opportunity to learn from expert instructors who are hiring managers and tech leads at Google, Facebook, Apple, and other top Silicon Valley tech companies. That’s not all! We offer domain-specific interview prep programs, including a tailor-made course for machine learning engineers.

Want to nail your next machine learning engineer interview? Sign up for our FREE Webinar.

We have put together the ultimate guide on everything you need to know about preparing for the Apple machine learning interview. Here’s what we will cover in this article:

• What is the difference between a machine learning engineer and an artificial intelligence engineer?
• What does an Apple machine learning engineer do?
• How to become a machine learning engineer at Apple
• What’s the Apple interview structure like for machine learning engineers?
• Interview study guide for Apple machine learning engineers
• Apple machine learning engineer interview questions
• Tips to crack your Apple machine learning engineering interview
• Machine learning engineering career FAQs

What Is the Difference Between a Machine Learning Engineer and an Artificial Intelligence Engineer?

Machine learning enables a computer to learn on its own or with little initial help. It uses four broad types of algorithms — supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Artificial intelligence uses three main techniques — searching techniques, knowledge representation, and reasoning.

A machine learning engineer uses machine learning techniques to solve real-life problems and build software. An artificial intelligence engineer uses artificial intelligence algorithms to solve the same problems.

What Does an Apple Machine Learning Engineer Do?

As an Apple machine learning engineer, you will be responsible for extracting value from available data at Apple, along with data collection, cleaning, preprocessing, training and deploying models, and production. Some of your responsibilities as an Apple machine learning engineer will be to:

• Analyze the ML algorithms to solve a given problem and rank them by their success probabilities.
• Explore and visualize data, then identify key differences in data distribution that could influence performance when deploying the model.
• Verify and ensure data quality via data cleaning. 
• Define validation strategies. 
• Define preprocessing or feature engineering for a given dataset. 
• Train models and tune their hyper-parameters. 
• Analyze errors of the model and design strategies for overcoming them. 
• Deploy models to production.

How to Become a Machine Learning Engineer at Apple

To become a machine learning engineer at Apple, you should have a bachelor's degree in computer science or equivalent with at least 5+ years of hands-on experience working with machine learning models.

Besides the educational qualifications and experience, here are some key qualifications you should consider working on before applying for the role:

• You should be proficient with Python and other basic libraries for machine learning such as Pandas and Scikit-Learn
• You should have expertise in visualizing and manipulating big datasets
• You should be familiar with Linux
• You should be familiar with a deep learning framework such as TensorFlow or Keras
• It will be an advantage if you have knowledge of database technologies
• You should have effective communication skills and the ability to communicate across teams and functions
• You should possess the ability to learn and apply new technologies through self-learning

What’s the Apple Interview Structure Like for Machine Learning Engineers?

Like most other FAANG companies, the Apple interview structure for a machine learning engineer role comprises a phone screen followed by on-site interviews. The interviewers are particularly interested in talking about your past projects with a special emphasis on deep learning and the implementation of machine learning concepts. 

Other questions will be based on coding skills, which will also test your optimization skills, time management, and space complexity management. 

Typically, the Apple machine engineer interview process consists of the following rounds:

• Phone screening: This round evaluates whether you are the right fit for the company before the hiring managers meet you in person. Phone screening is usually with HR and a machine learning engineer from the team at Apple you are interviewing for.
• Take-home tests: Once you clear the phone screening round, you may be invited for the take-home coding challenge, where you are given a coding assessment you need to solve in a given time.

• On-site interviews: Once you crack the coding assignment, the hiring managers will invite you for on-site rounds. There will be 4 to 5 rounds of on-site interviews with various committee members. Usually, a behavioral round is also part of the on-site interview round, during which the hiring managers assess your soft skills, self-awareness, and leadership abilities. 
  

Here’s a coding cheat sheet to help you prepare for your Apple machine learning engineer interview.

Interview Study Guide for Apple Machine Learning Engineers

Questions during a machine learning engineer interview cover a wide range of technical topics. We’ve put together some topics you should pay attention to during your ML tech interview prep:

Coding Topics

1. Programming languages
2. Data structures: Arrays, Trees, Stacks, Recursion
3. Algorithms: Binary Search, Insertion Sort, Bubble Sort, Selection Sort, Breadth-First Search
4. Object-oriented design
5. Databases
6. Distributed computing
7. Operating systems
8. Internet topics

System Design

1. Designing complex architecture systems and platforms
2. Product features

Behavioral Topics

1. Leadership
2. Why Apple?

Machine Learning Topics

1. General machine learning and artificial intelligence 
2. Model validation
3. Model optimization
4. Machine learning frameworks
5. Framing ML problems
6. Architecting ML solutions
7. Designing data preparation and processing systems
8. Automating and orchestrating ML pipelines
9. Monitoring, optimizing, and maintaining ML solutions
10. Deep learning frameworks
11. Machine learning applications

Data science promises to be the future of technology. While preferred skills keep changing with time, here are some essential ones you will need to brush up on while preparing for your Apple machine learning interview:

• Foundational coding
• Data science
• Python
• Deep learning
• Cloud offerings
• System design and software architecture
• Data structures
• Statistics/AB testing
• SQL
• Pro
• Databases
• Containerization
• Big O
• API development
• Project management
• MLOps
• Team management
• Leadership

Apple Machine Learning Engineer Interview Questions

Based on inputs from former candidates and hiring managers, we have created a study guide to help you prepare for your Apple machine learning interview:

SQL

SQL questions may need an aggregation with a filter, and others may need a few joins, recursions, and analytic functions. Following are a couple of sample SQL interview questions:

1. Analyze the given data on employees and departments of a company:

a) Employees:
Columns: id, first_name, last_name, salary, department_id
Types: int, varchar, varchar, int, int
b) Departments:
Columns: id, name
Types: int, varchar

From the above data, pick out the top 3 departments with a minimum of 10 employees and rank them as per the percentage of employees earning a salary of over $100,000. 

2. You’re given a dataset of a company’s employees and departments:

a) Employees
id – int
first_name – varchar
last_name – varchar
salary – int
department_id – int
b) Department
id – int
name – varchar

Using the information above, write an SQL query that selects the engineering department’s second-highest salary. Furthermore, your query should select the subsequent highest salary if more than one individual earns the highest salary. 

Operational Programming

To solve operational programming, you must know how to use arrays and dictionaries. Following are examples of problems you can expect:

1. Given a string and substring, find the number of times the substring occurs in the string.
2. Given a set of N words, where some words may be repeating. Count the number of occurrences of each word. (Order of the output must be the same as the input.) 

System Design Questions

1. Design a scheduler in Python
2. Design a ride-sharing application like Uber 

Recommended reading:How to Crack a System Design Interview

Algorithms and Data Structures

1. Find all palindromic decompositions of a given string s. (Solution)
2. Given a variety of coin types defining a currency system, find the minimum number of coins required to express a given amount of money. Assume an infinite supply of coins of every type. (Solution)
3. Sort a given singly linked list in ascending order. (Solution)

For more problems on data structure and algorithms, with solutions, visit the Problems page.

Other Machine Learning Interview Questions for Your Apple Interview

In addition to the above-listed type of questions, you can also expect some questions related to machine learning during the interview:

1. Predict the probability of a user clicking on a given post.
2. How would you build a system that detects if a given media is offensive?
3. In a data set comprising millions of users with hundreds of transactions each of thousands of products, how would you group them to form meaningful segments?
4. What is LRU Cache?
5. How will you process 100,000 files across multiple servers on Hadoop?
6. Let’s talk about the different kinds of memories in Java.
7. Create a market basket output using SQL.
8. How will you typically deal with failure analysis?
9. What do you know about random forests? Do you think Naive Bayes is better?

Related reading:Apple Interview Questions

Tips to Crack Your Apple Machine Learning Engineer Interview

Here are some additional essential tips to help you prepare for your machine learning engineer interview at Apple:

• Practice your coding skills on a whiteboard instead of only using paper or IDEs that provide syntax support and familiar formatting. This will make you feel more comfortable when you face the actual interview, as you won’t feel like a fish out of water. 
• Make sure to complete a couple of coding challenges to feel more confident during the technical rounds.
• Don’t forget to brush up on your soft skills. They are just as important as your technical mastery.
• If you have the slightest doubt about any questions during the interview, don’t hesitate to seek clarification. Remember, there is no such thing as a stupid question.
• While answering behavioral questions, resist the temptation of providing generic or scripted answers. Use the STAR method to structure your answers better and make it easy for the hiring managers to follow your chain of thought.
• Read up on Apple as a company and practice rethinking and redesigning Apple’s features that already exist.  
• Practice mock interviews on your own, with your peers, or take help from an expert like Interview Kickstart.
• You can also read the Apple Interview Process Guide for information.

Machine Learning Engineering Career FAQs

Q. How much does an Apple machine learning engineer make on average?

The average annual Apple machine learning engineer salary in the United States is $131,000, along with numerous perks and benefits. For more information, read Apple Machine Learning Engineer Salary.

Q. I have just started my software engineering career. Can I still become a machine learning engineer?

Machine learning positions at most tech companies are reserved for candidates with good experience (usually 3+ years) in the field. However, there are ways to start preparing yourself for a machine learning role early in your career. Experts at Interview Kickstart can show you how. Register for a free webinar today!

Get Ready for Your Dream Machine Learning Engineer Role

Think it is too difficult to crack Apple’s machine learning interview process? Not if you sign up with Interview Kickstart!

At IK, you’ll learn from instructors who are actively involved in the interview process at FAANG and other top tech companies. With detailed guidance from experienced instructors and interview coaches, you will be a step closer to grabbing your dream role in machine learning engineering. 

Our curriculum is one-of-its-kind and tailored to machine learning engineers to help you crack the toughest tech interviews at FAANG+ companies.

Want to know more? Register for a free webinar to take your career to the next level.