10X Genomics is a leading innovator in the field of genomics, dedicated to accelerating the mastery of biology through comprehensive and high-resolution single-cell analysis. This dynamic company plays a pivotal role in advancing scientific research and transformative medical breakthroughs.

The Machine Learning Engineer position at 10X Genomics is a critical role that involves developing cutting-edge machine learning models and algorithms to interpret complex biological data. Applicants are expected to have strong expertise in machine learning, programming, and data analysis to contribute to the company's mission of uncovering new insights in genomics.

In this guide, we'll help you navigate the interview landscape for this exciting position, providing you with key insights, sample interview questions, and valuable preparation tips to help you succeed. Let's dive in with Interview Query!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining 10X Genomics as a Machine Learning Engineer. Whether you were contacted by a 10X Genomics 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.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the 10X Genomics Talent Acquisition Team will make contact and verify key details like your experiences and skill levels. Behavioral questions may also be a part of the screening process.

In some cases, the 10X Genomics hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also engage 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 Machine Learning Engineer role at 10X Genomics usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around machine learning algorithms, data preprocessing, and model evaluation metrics.

In the case of the Machine Learning Engineer role, take-home assignments regarding building or evaluating models, and data cleaning may be incorporated. Apart from these, your proficiency in coding, working with libraries like TensorFlow or PyTorch, and understanding of theoretical concepts in machine learning may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Following a second recruiter call outlining the next stage, you will be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the 10X Genomics office. Your technical prowess, including algorithmic thinking, coding skills, and ML modeling capabilities, will be evaluated against the other 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 10X Genomics.

Quick Tips For 10X Genomics Machine Learning Engineer Interviews

• Understand the Core Products: 10X Genomics focuses heavily on genomic data. Make sure to understand their products and how machine learning can be applied in the context of genomics.
• Be Data-Driven: Assess your ability to extract meaningful insights from complex datasets since genomic data can be large and intricate. Brush up on your knowledge of data preprocessing and visualization.
• Reflect the Values: 10X Genomics prides itself on innovation and precision. Practice responding to behavioral questions with answers that demonstrate your commitment to quality and your passion for advancing scientific research.

Typically, interviews at 10X Genomics vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

• Write a query to get the last transaction for each day from a table of bank transactions. Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

• Write a function random_key that returns a key at random with a probability proportional to the weights. Given a dictionary with weights, write a function random_key that returns a key at random with a probability proportional to the weights.

• Write a function to get a sample from a standard normal distribution. Create a function to generate a sample from a standard normal distribution.

• Write an efficient function nearest_entries to find the closest element to N in a sorted list and return surrounding elements. Given a sorted list of integers ints with no duplicates, write an efficient function nearest_entries that takes in integers N and k and returns the element closest to N along with the k-next and k-previous elements of the list.

• What metrics/graphs/models would you use to analyze churn behavior for Netflix's pricing plans? Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What kinds of metrics, graphs, and models would you build to provide an overarching view of subscription performance?

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

• How would you value the benefit of keeping a hit TV show on Netflix? Netflix executives are considering renewing a deal with another TV network for exclusive streaming rights to a hit TV series. The show has been on Netflix for a year. How would you approach valuing the benefit of keeping this show on Netflix?

• How would you measure and address the success of LinkedIn’s newsfeed ranking algorithm?

• How would you measure the success of LinkedIn’s newsfeed ranking algorithm?

• If some success metrics for the newsfeed algorithm are increasing while others are decreasing, how would you approach this situation?

• How would you determine if the results of an AB test for a landing page redesign are statistically significant? We want to launch a redesign of a landing page to improve the click-through rate using an AB test. How would you infer if the results of the click-through rate were statistically significant or not?

• How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to a non-technical person. Focus on its role in determining the significance of results in an experiment or study.

• How many more samples would we need to decrease the margin of error from 3 to 0.3? Given a sample size (n) with a margin of error of 3, calculate the additional number of samples required to reduce the margin of error to 0.3.

• How would you determine if the results of an AB test on a landing page redesign are statistically significant? Describe the process of analyzing AB test results to infer if the click-through rate improvement from a landing page redesign is statistically significant.

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, how would you develop a model to identify which merchants the company should target for acquisition when entering a new market?

• How would you assign point values to letters in a Spanish Scrabble game without knowing Spanish? If you need to build Scrabble for Spanish users and don't know Spanish, how would you determine the point values for each letter?

What is the average salary for a Machine Learning Engineer at 10X Genomics?

Q: What is the interview process like for the Machine Learning Engineer position at 10X Genomics?
The interview process typically includes a phone screen with a recruiter, a technical phone interview, and an onsite interview. The onsite interview consists of several technical rounds, including coding, algorithm design, and machine learning problem-solving, as well as behavioral questions to assess cultural fit.

Q: What technical skills are essential for a Machine Learning Engineer at 10X Genomics?
You should have strong experience in machine learning algorithms, statistical modeling, and programming languages such as Python or R. Familiarity with data processing frameworks like TensorFlow, PyTorch, or similar tools is advantageous.

Q: What kind of projects do Machine Learning Engineers work on at 10X Genomics?
Engineers work on projects that involve large-scale genomic data processing and analysis, developing machine learning models for biological insights, and creating scalable data pipelines. They collaborate closely with scientists and bioinformaticians to push the boundaries of genomic data applications.

Q: How does 10X Genomics foster growth and innovation among its engineers?
10X Genomics values continuous learning and professional growth. The company offers opportunities to engage in cutting-edge research, attend industry conferences, and collaborate with world-class experts in genomics and data science. The work environment encourages curiosity, creativity, and knowledge sharing.

Q: How can I prepare for an interview at 10X Genomics?
Research the company and its products thoroughly, review fundamental machine learning and data science techniques, and practice coding problems relevant to the role. Utilize Interview Query to hone your skills and gain insights into common interview questions and scenarios. Be ready to discuss your previous experiences and how they align with the position’s requirements.

If you want more insights about the company, check out our main 10X Genomics Interview Guide, where we have covered many interview questions that could be asked. We've also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about 10X Genomics' interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every 10X Genomics machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!