Hugging Face is pushing the boundaries of Machine Learning to make it more accessible and impactful. Known for its open-source library of pre-trained models, Hugging Face is utilized by over 15,000 companies, including tech giants like Google, Salesforce, and Grammarly.

As a Machine Learning Engineer, you will enhance the open-source ecosystem by working with popular libraries such as Transformers, Datasets, and Accelerate. Collaborating with researchers, ML practitioners, and data scientists, you will engage with the community through GitHub, forums, and Slack.

Driven by a passion for open-source, Hugging Face values diverse backgrounds and experiences. Enjoy flexible working hours, health benefits, and equity as part of your compensation. Join a community dedicated to advancing ML and technology for the better.

If you aim to contribute to and shape the fast-growing ML landscape, this guide will walk you through the interview process and provide valuable insights. Let’s get started with Interview Query!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Hugging Face as a Machine Learning Engineer. Whether you were contacted by a Hugging Face recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences, particularly those related to open-source contributions and machine learning.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Hugging Face Talent Acquisition Team will make contact and verify key details, such as your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Hugging Face hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Hugging Face Machine Learning Engineer role is usually conducted through virtual means, including video conferences and screen sharing. Questions in this 1-hour long interview stage may revolve around Hugging Face’s open-source libraries such as Transformers, Datasets, or Accelerate, as well as specific technologies like PyTorch or TensorFlow.

In some cases, take-home assignments regarding specific machine learning tasks, optimization problems, or model implementations may be incorporated. Your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Hugging Face office or through multiple virtual meetings if the role is remote. Your technical prowess, including programming and modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Machine Learning Engineer role at Hugging Face.

Quick Tips For Hugging Face Machine Learning Engineer Interviews

• Know the Ecosystem: Hugging Face is at the forefront of the open-source machine learning ecosystem. Familiarize yourself with their libraries like Transformers, Datasets, or Accelerate by going through their GitHub repositories and documentation.
• Be Community-Driven: Given the open-source nature of Hugging Face, demonstrating your involvement in open-source projects and articulating how you have engaged with communities can be a big plus.
• Embody the Mission: Hugging Face aims to make complex machine learning accessible. Be prepared to discuss how you can bring value to this mission, and present ideas on how you might further their initiatives.

Typically, interviews at Hugging Face vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity of your solution.

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. The solution should have a complexity of (O(n)).

• Develop a function precision_recall to calculate precision and recall metrics. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array and return its index, or -1 if the value is not found. The algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why would job applications decrease while job postings remain constant on a job board? You observe that the number of job postings per day has remained stable, but the number of applicants has been decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Based on this outcome, determine if the coin is fair.

• Write a function to calculate sample variance from a list of integers. Create a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places.

• Would you trust the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you find anything suspicious about these results?

• How to find the median in a list with more than 50% repeating integers in O(1) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in O(1) computational time and space.

• What are the drawbacks and formatting changes needed for messy datasets? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in messy datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss the advantages of using random forest compared to logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to evaluate the model's accuracy and validity?

What is the average salary for a Machine Learning Engineer at Hugging Face?

Q: What does the Machine Learning Engineer role at Hugging Face involve?

As an open-source Machine Learning Engineer at Hugging Face, you will work to improve the open-source machine learning ecosystem. This includes working with libraries like Transformers, Datasets, or Accelerate, engaging with researchers, ML practitioners, and data scientists, and fostering a vibrant ML community. You'll brainstorm with the team to focus on projects that interest you and have a significant impact.

Q: What kind of candidates is Hugging Face looking for?

Hugging Face is seeking candidates who are passionate about open-source and making complex technology more accessible. Experience with PyTorch and/or TensorFlow, building and optimizing models, and contributing to fast-growing ML libraries is desirable. The company values diversity and encourages applicants from various backgrounds, even if they don't meet every single requirement.

Q: What benefits does Hugging Face offer its employees?

Hugging Face offers flexible working hours, remote options, health, dental, and vision benefits for employees and their dependents, generous parental leave, and unlimited paid time off. Employees also receive reimbursement for relevant conferences, training, and educational opportunities. Additionally, all employees have company equity as part of their compensation package.

Q: How does Hugging Face support the community and its employees' professional development?

Hugging Face actively supports the ML/AI community by fostering collaboration and maintaining one of the most active machine learning communities. The company values continuous growth and professional development, offering reimbursement for conferences, training, and education. Employees get to interact with smart and passionate people in the industry, continually challenging themselves to make significant impacts.

Q: How can I prepare for the interview at Hugging Face?

To prepare for an interview at Hugging Face, familiarize yourself with their open-source libraries like Transformers and Datasets. Also, practice common technical interview questions using Interview Query to fine-tune your problem-solving and technical skills. Be ready to discuss your experience with machine learning models and your enthusiasm for contributing to the open-source community.

If you desire a role where you contribute significantly to advancing machine learning, consider applying for a position at Hugging Face. Here, you will be part of a rapidly-growing organization, known for its open-source libraries and vibrant community. You'll be collaborating with some of the brightest minds in the industry, working on impactful projects, and fostering a culture that values diversity, equity, and inclusivity.

Ready to dive deeper into Hugging Face? Check out our Hugging Face Interview Guide on Interview Query for extensive insights into potential interview questions and processes. We’ve also crafted interview guides for roles such as software engineer and data analyst, helping you navigate different paths at Hugging Face with confidence.

At Interview Query, our mission is to turbocharge your interview readiness with comprehensive resources, giving you the edge to ace every Hugging Face machine learning engineer interview challenge.

Explore all our company interview guides for thorough preparation. Got questions? We're here to help.

Good luck with your interview!