Apple Machine Learning Engineer Interview Questions + Guide in 2024Apple Machine Learning Engineer Interview Questions + Guide in 2024


Research topics in machine learning at Apple are vast and diverse, from health personalization to natural language understanding. Therefore, Apple continuously seeks machine learning talent to contribute to these research projects.

If you’re preparing for a machine learning interview at Apple, you’ve come to the right place. In this interview guide, we’ll walk you through the machine learning engineer interview process at Apple with our selected questions, strategies, and interview tips. Without further ado, let’s dive in!

What Is the Interview Process Like for a Machine Learning Engineer Role at Apple?

A machine learning engineer interview at Apple will vary in duration and format. The number of stages you’ll undergo during the interview process depends on the location and your experience level. However, you can generally expect the following stages during your interview journey.

Step 1: Initial Screening

If your application shows you fulfill the job requirements, you’ll be invited to a screening session with one of the hiring teams. The questions asked in this stage primarily relate to your resume and past experiences.

They will also ask about your motivation for applying and your expectations of the role. Some domain-specific questions might be asked as well. If you’re applying for a machine learning position with a focus on NLP, then expect some technical questions about NLP.

Step 2: ML Fundamental Assessment

The machine learning engineer position is very technical. Therefore, in this second stage, you’ll be asked questions related to machine learning concepts such as bias-variance trade-offs, the inner workings of a machine learning model, and common activation functions.

This round typically lasts 45 minutes to an hour.

Step 3: Coding Assessment

Once you pass the theoretical assessment, you’ll undergo a coding examination, where you do a live coding session to solve algorithmic questions or build a machine learning model given a particular use case.

This round typically lasts 45 minutes to an hour.

Step 3: Behavioral Assessment

This round is the final step in the interview process at Apple. The questions in this stage typically revolve around a case study and your approach to solving interpersonal problems. They will also ask questions that test your time management and problem-solving skills.

In this round, show how much you want to work at Apple. Prepare several questions that you can ask, as this interview is also your chance to learn about Apple.

What Questions Are Asked at Apple’s Machine Learning Engineer Interview?

The questions in a machine learning engineer interview can be divided into two parts: technical and behavioral.

Technical questions will test your knowledge of various concepts necessary for success in this role, such as Python programming, machine learning concepts, statistics, and data structures. Behavioral questions will assess your motivation and interpersonal skills.

1. How do you make sure projects and tasks stay on schedule?

At Apple, sometimes you’ll be working on multiple tasks with tight deadlines. Therefore, time management is a must-have skill if you want to become a machine learning engineer at this company.

How to Answer

Highlight your organizational skills, project management methodologies, and communication strategies to ensure projects and tasks stay on schedule. Provide examples of when you successfully managed projects and met deadlines.

Example answer:

“To ensure that projects and tasks stay on schedule, I use several strategies. First, I set clear project timelines based on the complexity of the work involved. I break down larger projects into smaller, manageable tasks and assign deadlines to each. Also, I use project management tools like Asana or Jira to track progress and identify potential bottlenecks.

Regular communication with stakeholders and team members is crucial. I schedule regular check-in meetings to talk about progress, address any challenges, and adjust timelines as needed.”

2. What makes you a good fit for our company?

This question is asked in an Apple machine learning engineer interview to assess your values and their alignment with the role.

How to Answer

Highlight your relevant skills, experiences, and values that align with Apple’s. Research the company’s mission, products, and recent initiatives to emphasize how your background and expertise can contribute to their success.


“I believe I’m a good fit for Apple because of my strong background in machine learning and my passion for innovation and technology. Throughout my career, I’ve had a track record of successfully tackling complex problems and developing cutting-edge solutions.

For example, in my previous role, I led a team in developing a novel recommendation algorithm that significantly improved user engagement and retention rates. I’m also highly cooperative and thrive in fast-paced environments, which I understand are key aspects of Apple’s culture.”

3. How do you keep informed of developments in machine learning?

Keeping current with the latest advancements in AI is crucial if you’d like to become a machine learning engineer at Apple. They look for engineers who demonstrate a proactive approach to continuous learning.

How to Answer

Emphasize your dedication to continuous learning and highlight specific methods you use to stay informed about developments in machine learning.


“I like to keep myself updated on the developments in machine learning through various channels. For example, I regularly read research papers published in top-tier conferences and journals such as NeurIPS, ICML, and arXiv.

I also allocate time to online courses, workshops, and webinars to deepen my understanding of specific topics and acquire new skills. Lastly, I work on personal projects and experiments to apply a popular method to improve the response generated from LLMs.”

4. Tell me a time when your colleagues disagreed with your approach. What did you do to bring them into the conversation and address their concerns?

At Apple, you’ll work alongside colleagues with differing expertise and opinions. You need to be able to collaborate effectively and navigate disagreements professionally.

How to Answer

Provide an example of a disagreement between you and your colleagues that you successfully resolved. Focus on your steps to understand their perspectives, facilitate open communication, and find a mutually agreeable solution.


“In a previous project, my colleagues and I had differing opinions on the best approach to optimizing our machine learning model for performance. While some team members preferred a traditional algorithmic approach, I advocated for using deep learning techniques for better accuracy.

To address their concerns and foster cooperation, I scheduled a team meeting to openly discuss our viewpoints. I actively listened to their feedback, recognizing the validity of their concerns while also presenting evidence supporting the value of my proposed approach. Through constructive dialogue and data-driven analysis, we reached a consensus to experiment with both approaches and evaluate their performance objectively.”

5. Tell me about a time when something went wrong at work and you took control.

Unexpected challenges and setbacks are common occurrences in a highly complex research project. Therefore, Apple values machine learning engineers who can remain calm under pressure, take initiative, and lead when faced with difficult situations.

How to Answer

Provide an example of when something went wrong at work and you took control of the situation to resolve it. Describe the situation, including what went wrong, your steps to address the issue, and the outcome.


“In a previous role as a machine learning engineer, we encountered a critical bug in our model deployment pipeline that caused errors and delays in delivering predictions to our end users. As the lead engineer on the project, I immediately took control by identifying the root cause of the issue through comprehensive debugging and analysis of the codebase. I then organized a meeting with the team to discuss the problem, communicate the urgency of the situation, and delegate tasks to address specific components of the pipeline.

I ensured clear communication channels were established between team members and stakeholders to provide regular updates. I also collaborated with the DevOps team to implement temporary workarounds to minimize the impact on our users while we worked on a permanent fix.”

6. We have two models: one with 85% accuracy and one with 82%. Which one do you pick?

Critical thinking and the ability to make informed decisions are desirable traits of a machine learning engineer. This question is asked to gauge your thought process for assessing a machine learning model’s performance.

How to Answer

Consider factors beyond accuracy, such as precision, recall, and the application’s specific requirements. Discuss the importance of evaluating multiple performance metrics and weighing them based on the task’s objectives and constraints.


“In choosing between two models with 85% and 82% accuracy, I would first assess additional performance metrics beyond accuracy, such as precision, recall, and F1 score, to gain a comprehensive understanding of each model’s strengths and weaknesses. I would also consider the specific requirements of the application and any constraints related to computational resources or model interpretability. Likewise, I would conduct thorough testing and validation, including cross-validation and evaluation on a holdout dataset, to ensure the selected model generalizes well to unseen data and performs reliably in real-world scenarios.”

7. Can you describe a few examples of neural network architectures used for semantic segmentation?

Machine learning engineers at Apple are expected to keep up with recent advancements in deep learning methods. So, expect questions to test your awareness of current state-of-the-art models in different use cases.

How to Answer

Mention different neural network architectures commonly used for semantic segmentation tasks. Then, discuss the key features, advantages, and limitations of each architecture.


“Semantic segmentation involves the pixel-level classification of images, and various neural network architectures have been developed to solve this problem effectively. Some examples include:

  1. U-Net: U-Net is a popular architecture known for its symmetric encoder-decoder structure. It is widely used in medical image analysis, autonomous driving, and satellite image segmentation due to its ability to capture detailed spatial information and handle small objects effectively.
  2. FCN (Fully Convolutional Network): FCN is one of the pioneering architectures for semantic segmentation. It replaces fully connected layers with convolutional layers to enable end-to-end pixel-wise prediction. FCN is versatile and has been applied to various domains, including object detection, scene parsing, and image segmentation.
  3. DeepLab: DeepLab is a family of convolutional neural network architectures known for their effectiveness in semantic segmentation tasks. DeepLab variants such as DeepLabv3+ and DeepLabv3++ incorporate atrous convolution and spatial pyramid pooling to capture both global and local contextual information, making them suitable for high-resolution images and challenging datasets.”

8. Let’s say that you’re training a classification model. How would you combat overfitting when building tree-based models?

During a machine learning engineer interview at Apple, expect questions related to general machine learning concepts such as overfitting, bias-variance trade-off, and the difference between boosting and bagging.

How to Answer

Discuss various techniques used to combat overfitting in tree-based models, such as pruning, regularization, and ensemble methods. Explain how each technique encourages better generalization to unseen data.


“In tree-based models, overfitting can occur when the model captures noise in the training data rather than learning the underlying patterns. To combat overfitting in a tree-based model, several techniques can be used. Pruning involves removing parts of the tree that do not provide significant improvement in model performance, thereby simplifying the model and reducing overfitting. Limiting the maximum depth of the tree can also prevent overfitting by constraining the model’s complexity. Ensemble methods like random forests or gradient boosting combine multiple weak learners to create a stronger, more generalized model, reducing the risk of overfitting.”

9. What is the curse of dimensionality and the problems it might cause?

The curse of dimensionality is a common phenomenon when working with huge datasets. Your awareness of this topic is critical to developing models effectively.

How to Answer

Explain the curse of dimensionality and its potential consequences in machine learning tasks. Discuss how increasing the number of features or dimensions in a dataset can lead to sparsity, computational inefficiency, and overfitting problems.


“The curse of dimensionality refers to the challenges that arise when working with high-dimensional data, where the number of features or dimensions exceeds the number of observations.

One problem caused by the curse of dimensionality is data sparsity. As the number of dimensions increases, the available data points become increasingly sparse in high-dimensional space, making it difficult to estimate meaningful relationships between variables accurately.

The curse of dimensionality can cause overfitting issues in machine learning models. With a high-dimensional feature space, models may capture noise or irrelevant features in the training data, resulting in poor generalization performance on unseen data.”

10. Given an array of integers, how can you write a function that moves all zeros to the end of the array?

Writing efficient algorithms is a skill that a machine learning engineer at Apple needs to possess. This question will test your knowledge of data structures and your ability to quantify the time complexity of your code.

How to Answer

Write a function that iterates through the array, moving all zeros to the end while maintaining the order of non-zero elements. Consider edge cases, such as when the array is empty or contains no zeros.


“The following function iterates through the array once, moving non-zero elements to the front while keeping track of the index where the next non-zero element should be placed. Then, it fills the remaining positions with zeros, effectively moving all zeros to the end of the array. The time complexity of this solution is O(n), where n is the length of the array.”

def move_zeros_back(array):
    non_zero_index = 0
    for num in array:
        if num != 0:
            array[non_zero_index] = num
            non_zero_index += 1
    for i in range(non_zero_index, len(array)):
        array[i] = 0
    return array

array = [0, 5, 4, 2, 0, 3]
print(move_zeros_back(array))  # Output: [5, 4, 2, 3, 0, 0]

11. Can you explain the concept of a bounding box regressor in object detection?

Apple’s products often incorporate advanced machine learning and computer vision technologies. Therefore, expect this kind of question if you’re applying for a machine learning engineer position with a focus on computer vision.

How to Answer

Explain how bounding box regression works, including its components and role in localizing objects within images.


“Bounding box regression is a fundamental technique used to localize objects within images, which is crucial for various applications such as augmented reality and image recognition.

In object detection tasks, the goal is to identify and localize objects of interest within images. Bounding box regression involves predicting the coordinates of a bounding box that tightly encloses an object. This is typically achieved using machine learning algorithms, such as convolutional neural networks (CNNs), which learn to regress the coordinates of the bounding box based on input image features.”

12. Given two strings, how can you write a function to return True if the strings are anagrams of each other and False if they are not?

If you’re applying for a machine learning engineer position at Apple with a focus on NLP, you need to understand string manipulation. This question will also test your logical thinking and problem-solving skills.

How to Answer

Write a function that checks if both strings have the same set of characters (ignoring spaces and case) and the same frequency of each character. Make use of a dictionary to count the occurrences of each character in both strings and then compare the dictionaries.


“This function first removes spaces and converts both strings to lowercase to ensure case-insensitive comparison. Then, it counts the occurrences of each character in both strings using dictionaries and compares the dictionaries to determine if the strings are anagrams. The time complexity of this solution is O(n), where n is the length of the longer string.”

def are_anagrams(string_1, string_2):
    # Remove spaces and convert to lowercase
    string_1 = string_1.replace(" ", "").lower()
    string_2 = string_2.replace(" ", "").lower()
    # Check if lengths are equal
    if len(string_1) != len(string_2):
        return False
    # Count occurrences of each character in both strings
    char_count_1 = {}
    char_count_2 = {}
    for char in string_1:
        char_count_1[char] = char_count_1.get(char, 0) + 1
    for char in string_2:
        char_count_2[char] = char_count_2.get(char, 0) + 1
    # Compare the dictionaries
    return char_count_1 == char_count_2

print(are_anagrams("listen", "silent"))  # Output: True
print(are_anagrams("banana", "bandana"))  # Output: False

13. Can you explain what auto-regression is?

Some Apple products, such as Maps and Apple Watch, rely on advancements in time series analysis. If you’re applying for a machine learning engineer position with a focus on time series, expect questions on time series forecasting.

How to Answer

Explain what auto-regression is and how it is used in time series analysis. Highlight its significance in modeling sequential data.


“Auto-regression is a statistical method used to model the relationship between a variable and its lagged values in a time series dataset. In a nutshell, it involves predicting the future values of a variable based on its past values.”

14. Given a JSON string with nested objects, how can you write a function flatten_json that flattens all the objects to a single key-value dictionary?

Data that you’ll use to train a machine learning model at Apple comes in various formats, and JSON is one of the most common data structures out there. Therefore, you need to understand how to handle data in JSON format during data preprocessing and feature preparation steps.

How to Answer

Write a function that recursively traverses the JSON structure and concatenates keys to form new keys for the flattened dictionary. It’s important to handle cases where values are themselves dictionaries and recursively flatten them.


*“The following function recursively traverses the nested JSON structure and flattens it into a single key-value dictionary. The _flatten_json_helper function handles the recursion by concatenating keys with a specified separator. The time complexity of this solution is O(n), where n is the total number of keys in the nested JSON structure.”*

import json

def flatten_json(json_str):
    def _flatten_json_helper(dictionary, parent_key='', separator='_'):
        items = []
        for key, value in dictionary.items():
            new_key = parent_key + separator + key if parent_key else key
            if isinstance(value, dict):
                items.extend(_flatten_json_helper(value, new_key, separator=separator).items())
                items.append((new_key, value))
        return dict(items)

    # Convert JSON string to Python dictionary
    json_dict = json.loads(json_str)
    # Flatten the dictionary recursively
    flattened_dict = _flatten_json_helper(json_dict)
    # Convert flattened dictionary back to JSON string
    return json.dumps(flattened_dict)

json_str = json.dumps({'a':{'b':'c', 'd':'e'}})
print(flatten_json(json_str))  # Output: '{"a_b": "c", "a_d": "e"}'

15. What is normal distribution, and why do we care about it?

What a machine learning model does under the hood is purely statistics. Therefore, you need to understand statistics fundamentals to apply as a machine learning engineer at Apple.

How to Answer

Explain what normal distribution represents, its properties, and its significance in statistical analysis and machine learning.


“Normal distribution is a symmetric probability distribution characterized by its bell-shaped curve. It’s widely used in statistics because many real-life data tend to follow a normal distribution. In machine learning, normal distribution is significant because several algorithms, like linear regression and Gaussian Naive Bayes, assume that the features or errors follow a normal distribution.”

16. Say you work at a major credit card company and are given a dataset of 600,000 credit card transactions. How can you use this dataset to build a fraud detection model?

A machine learning engineer at Apple needs to be creative enough to conceptualize an approach before actually implementing it. This question will test your ingenuity in solving a hypothetical problem before implementation.

How to Answer

Outline how you would build a fraud detection model using the given dataset. This includes data preprocessing steps and the selection of appropriate machine learning algorithms. Mention also techniques for model evaluation and performance optimization.


“I would first start by performing extensive data preprocessing. This includes handling missing values, scaling numerical features, and encoding categorical variables. Next, I would explore the dataset to understand its distribution and identify potential patterns or anomalies that could indicate fraudulent transactions.

Once the data is preprocessed, I would proceed to select suitable machine learning algorithms for classification tasks, considering the nature of the dataset and the complexity of the fraud detection problem. Common algorithms for fraud detection include logistic regression, decision trees, random forests, and gradient boosting. I would then train multiple models using a portion of the dataset and evaluate their performance using appropriate metrics such as precision, recall, and F1-score. Additionally, I would address class imbalance issues by applying techniques such as oversampling, undersampling, or using ensemble methods like SMOTE.”

17. Can you give examples of when a false positive is more important than a false negative?

When developing a machine learning model, a machine learning engineer needs to understand different kinds of metrics to evaluate its performance. They also need to be able to choose the appropriate metrics and interpret the results to ensure the model’s reliability.

How to Answer

Explain the definition of false positives and false negatives. Then, provide examples of situations in which false positives are more critical than false negatives.


“In some situations, false positives can have more significant consequences than false negatives. For example, in security screening at airports, a false positive result indicating the presence of a threat when there is none can lead to unnecessary delays and inconvenience for passengers. However, a false negative result failing to detect a genuine threat could have catastrophic consequences. Similarly, in medical diagnosis, a false positive for a life-threatening condition could lead to unnecessary treatments and stress for patients, but a false negative could result in a failure to treat a severe illness, potentially leading to adverse outcomes.”

18. How can you write a function calculate_rmse to calculate the root mean squared error of a regression model, given the predictions and target values as arguments?

Evaluating model performance will be a common task as a machine learning engineer at Apple. However, in some use cases, you need to code your own evaluation metrics instead of relying on popular frameworks like PyTorch or TensorFlow.

How to Answer

Explain that RMSE measures the average deviation of the predicted values from the actual values, with lower RMSE indicating better model performance. When implementing the function, iterate through the predicted and true values, calculate the squared differences, compute the mean of these squared differences, and finally take the square root of the mean to obtain the RMSE.


“RMSE measures the average deviation of predicted values from the actual values, providing a comprehensive assessment of model accuracy. To calculate RMSE in code, I would start by iterating through the predicted and true values provided as input. Then, I would calculate the squared difference between them and store the result. After iterating through all the pairs, I would compute the mean of these squared differences and take the square root of it to obtain the RMSE value.”

import math

def calculate_rmse(y_pred, y_true):
    # Check if the lengths of y_pred and y_true are equal
    if len(y_pred) != len(y_true):
        raise ValueError("Lengths of y_pred and y_true must be equal.")
    # Calculate squared differences and sum them
    sum_squared_diff = sum((pred - true) ** 2 for pred, true in zip(y_pred, y_true))
    # Calculate the mean of squared differences
    mean_squared_diff = sum_squared_diff / len(y_pred)
    # Calculate RMSE by taking the square root of the mean squared difference
    rmse = math.sqrt(mean_squared_diff)
    return rmse

y_pred1 = [3, 4, 5]
y_true1 = [3, 4, 5]
print("Example 1 RMSE:", calculate_rmse(y_pred1, y_true1))

19. What happens if we have correlated features in the dataset?

At Apple, you’ll be working with big data, and having features that correlate with each other is common. Therefore, machine learning engineers at Apple need to understand the impact of correlated features on a machine learning model’s performance.

How to Answer

Explain how correlated features can affect machine learning models and discuss strategies for addressing this issue, such as feature selection, dimensionality reduction, or regularization techniques.


“Correlated features can lead to multicollinearity, where predictor variables are highly correlated with each other, affecting the model’s interpretability and performance. In machine learning, multicollinearity can cause instability in parameter estimation and make the model sensitive to small changes in the training data. To mitigate this issue, various techniques can be implemented, such as feature selection methods.

Additionally, dimensionality reduction techniques like principal component analysis (PCA) can be used to transform correlated features into a new set of orthogonal features while preserving the most important information. Regularization methods like LASSO or ridge regression can also penalize the coefficients of correlated features, reducing their influence on the model.”

20. How would you choose the k value when using k-means clustering?

Knowledge of the inner workings of various machine learning algorithms is very desirable at Apple. This is because you’re often going to develop complex algorithms from scratch.

How to Answer

Explain the importance of choosing the optimal value of k in k-means clustering. Mention common methods used for selecting k, such as the elbow method and silhouette score.


“One commonly used method for selecting k is the elbow method, which involves plotting the within-cluster sum of squares (WCSS) against the number of clusters and identifying the ‘elbow’ point where the rate of decrease in WCSS slows down.

Another approach is the silhouette score, which measures the quality of the clusters by considering both the cohesion within clusters and the separation between clusters.”

How to Prepare for a Machine Learning Engineer Interview at Apple

Here are some tips to help you gain a competitive edge in your interview for a machine learning engineer position at Apple.

Study the Company and Role

Securing a position at Apple is quite tough. Therefore, you need to research the company and the role before the interview process. Find out what Apple is currently up to and the variety of machine learning topics available at the company. This will enable you to tailor your application documents appropriately.

In fact, Apple has a dedicated resource on its website that provides a sneak peek at the advancements they’re currently working on. To see what they’re doing in machine learning research, check out their dedicated blog.

Brush Up on Technical Skills

Expect some tough technical questions during your machine learning engineer interview at Apple.

To help you brush up on the necessary skills for a machine learning engineer position, we offer several learning paths, such as Python, data science, and modeling & machine learning. To further practice your problem-solving and algorithmic skills, try solving several questions in our question banks or challenges.

One more tip: if you’re overwhelmed by the wide variety of topics to learn, concentrate on the specific tools or skills mentioned in the job description. At Apple, you’re typically expected to focus on a particular field of machine learning, which narrows down your learning path during interview preparation.

Do Personal Projects

Machine learning engineers at Apple are expected to keep themselves updated with the latest advancements in AI. Consider preparing personal projects to show the hiring teams your commitment to continuous learning. Kudos to you if your project is closely related to the field to which you’re applying. You’ll be glad you put in the work since your personal project can serve as an engaging discussion point during your interview.

Personal projects also enhance your problem-solving ability as you need to implement different concepts and solve bugs throughout the project. For ideas on personal project topics, check out our take-home challenges.

Practice Communication Skills

A machine learning engineer needs to be able to explain complex concepts in an understandable way. Therefore, it’s a no-brainer that you need to practice your communication skills. One effective way to do this is through a mock interview.

With a mock interview, you’ll receive personalized feedback on your interview performance from a fellow data enthusiast. If you’re looking for an opportunity to conduct a mock interview, consider joining the mock interview service on our platform.

If you need one-on-one coaching with an expert to improve your communication or machine learning skills, check out the coaching service offered on our platform.

Frequently Asked Questions

Below are some frequently asked questions about working as a machine learning engineer at Apple.

What is the average salary for a machine learning engineer role at Apple?


Average Base Salary


Average Total Compensation

Min: $125K
Max: $225K
Base Salary
Median: $176K
Mean (Average): $175K
Data points: 682
Min: $14K
Max: $481K
Total Compensation
Median: $245K
Mean (Average): $250K
Data points: 97

View the full Machine Learning Engineer at Apple salary guide

The average base salary for a machine learning engineer at Apple is $174,877. In comparison, the average salary for a machine learning engineer position in the US is $148,747. This means that you’ll be compensated above the market average at Apple.

For more insights into the salary range of a machine learning engineer at various companies segmented by experience level and location, check out our comprehensive Machine Learning Engineer Salary Guide.

What jobs at other companies can I apply for besides Apple’s machine learning engineer role?

If you want to work in a tech company with a similar research area in AI as Apple, consider Microsoft, Google, or Amazon. We have guides for each company to help your preparation process.

For insights on data-related roles in other tech companies, check out our comprehensive company interview guides.

Does Interview Query have job postings for Apple’s machine learning engineer position?

Yes, we have job postings for a machine learning engineer position at Apple, which you can apply for directly through our job portal. If you’re also interested in opportunities for a machine learning engineer position at other companies, our job board provides an updated list of available positions.

However, we also suggest you check out Apple’s official careers page to explore their most recent openings for machine learning engineers or other data-related roles.


To ace Apple’s machine learning engineer interview, you need to demonstrate proficiency in two aspects: technical and behavioral. Common technical interview questions for this role at Apple include statistics, data science, machine learning concepts, and programming. Behavioral questions revolve around a case study that could provide insights into your personality, work ethic, and problem-solving skills.

If you’re keen on understanding the interview processes for other data-related roles at Apple, we’ve got you covered. Be sure to check out our Apple guides for business analyst, business intelligence, data analyst, data engineer, data scientist, growth marketing analyst, research scientist, and software engineer interviews.

We hope that this article helps you prepare for the machine learning engineer interview at Apple. If you have any questions, contact us on our platform!