Clarifai is a pioneering deep learning AI platform that excels in computer vision, natural language processing, and audio recognition. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai quickly emerged as an industry leader by securing the top five positions at the 2013 ImageNet Challenge. Known for converting unstructured images, video, text, and audio data into structured data with remarkable speed and accuracy, the company operates globally with a diverse remote workforce.

As a Research Scientist at Clarifai, you will be at the forefront of transforming cutting-edge academic research into practical, scalable solutions. This role involves collaborating with various teams to enhance existing products and explore new AI capabilities. With expertise in deep learning, particularly in computer vision, NLP, and multi-modal AI, you will drive innovation and have a substantial impact within both the developer community and the broader field of AI/ML. If you're motivated by seeing your ideas implemented and thrive in a collaborative, inclusive environment, this position offers a rewarding opportunity to shape the future of AI.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Clarifai as a Research Scientist. Whether you were contacted by a Clarifai 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 Clarifai Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Clarifai 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 Clarifai Research Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around deep learning, computer vision, NLP, and multi-modal AI.

In the case of research roles, take-home assignments or research presentations regarding specific technical challenges may be incorporated. Apart from these, your proficiency against neural network frameworks, coding skills in Python, and practical problem-solving 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

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 Clarifai office. Your technical prowess, including programming and deep learning 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 Research Scientist role at Clarifai.

Quick Tips For Clarifai Research Scientist Interviews

• Understand Clarifai's AI Platform and Products: Familiarize yourself with Clarifai's offerings in computer vision, NLP, and multi-modal AI. Study how these products are utilized by clients and think about potential improvements or new applications.

• Stay Updated on Academic Research: Given the nature of the Research Scientist role, staying current with the latest research papers and attending relevant conferences will help you in discussions about practical applications of academic insights.

• Be Prepared for Deep Technical Assessments: Brush up on deep learning frameworks, especially PyTorch and TensorFlow. Ensure you understand coding best practices and are ready to discuss past projects and your role in them.

Typically, interviews at Clarifai vary by role and team, but commonly Research Scientist 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.

• Write a function to find the missing number in an array of integers spanning 0 to n. 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. Complexity of (O(n)) required.

• Write a function to calculate precision and recall metrics from a 2-D matrix. 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 not found. Bonus: Your 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 might the number of job applicants be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily 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 problems in "messy" datasets.

• Is this a fair coin based on 10 flips resulting in 8 tails and 2 heads? You flipped a coin 10 times, resulting in 8 tails and 2 heads. Determine if this outcome suggests the coin is fair.

• How do you write a function to calculate sample variance for a list of integers? Write a function that calculates the sample variance for a given list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything suspicious about an A/B test with 20 variants where one is significant? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these findings.

• How do you find the median of a list where over 50% of the elements are the same in O(1) time and space? Given a sorted list of integers where more than 50% of the list is the same integer, write a function to return the median value in (O(1)) computational time and space. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them for better analysis? You have student test score data in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems in "messy" datasets. Example datasets are provided in the image link.

• 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 over 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 for a business problem 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? 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 its accuracy and validity?

What is the average salary for a Research Scientist at Clarifai?

Q: What is Clarifai?
Clarifai is a leading AI platform specializing in deep learning for computer vision, natural language processing, and audio recognition. Founded in 2013 by Matt Zeiler, Ph.D., the company enables organizations to transform unstructured data into structured data more quickly and accurately than human capabilities.

Q: What does a Research Scientist at Clarifai do?
As a Research Scientist at Clarifai, you'll apply cutting-edge academic insights to practical applications, pushing the boundaries of AI. Your role involves rapid prototyping, collaborative innovation, and leading initiatives in CV, NLP, and multi-modal AI. You'll work closely with various teams to enhance products and pioneer new AI capabilities.

Q: What are the key responsibilities for this position?
Your responsibilities include leading R&D initiatives, engaging in rapid prototyping, writing maintainable code, adhering to best engineering practices, and fostering a collaborative environment. You'll continuously learn and share knowledge to advance Clarifai’s innovation and impact.

Q: What qualifications are required for the Research Scientist position?
Candidates should have 3+ years of hands-on experience in neural network development, expertise in Python, and proficiency with libraries like PyTorch. An advanced degree (Ph.D. or Master's) in Computer Science, Mathematics, or a related field, and the ability to obtain a Secret or Top Secret Clearance are also required.

Q: How can I prepare for an interview at Clarifai?
To prepare for an interview at Clarifai, research the company, review your technical skills, and practice common interview questions using Interview Query. Focus on your deep learning expertise and be ready to discuss collaborative projects and innovative problem-solving.

Embarking on a career as a Senior Research Scientist at Clarifai is a unique opportunity to transform groundbreaking AI research into tangible products that impact users worldwide. With a strong focus on collaboration and continual learning, you'll be at the forefront of innovation in computer vision, NLP, and multi-modal AI, pushing the boundaries of what's possible.

If you want more insights about the company, check out our main Clarifai 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 Clarifai’s 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 Clarifai 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!