Babel Street is a pioneering company dedicated to illuminating identity and information for a safer, more productive world. Their AI-powered products and advanced data analytics platform provide actionable insights that safeguard lives and protect critical assets across numerous high-stakes industries, including financial services, healthcare, and law enforcement.

As a Machine Learning Engineer at Babel Street, you will work in their AI research team based in Somerville, MA. The role bridges research, software engineering, and production DevOps. Responsibilities include optimizing models for performance, developing containerized ML solutions, and collaborating with DevOps for deployment.

To excel in this position, proficiency in Python, experience with cloud infrastructure, and familiarity with containerization technologies are essential. Learn more about this opportunity and prepare for your interview using the resources available on Interview Query.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Babel Street as a Machine Learning Engineer. Whether you were contacted by a Babel Street 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 Babel Street 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 Babel Street 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 Babel Street Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Babel Street’s data systems, machine learning models, and coding skills.

In the case of technical roles, take-home assignments regarding algorithm design, optimization, and ML model deployment are incorporated. Apart from these, your proficiency against cloud infrastructures, containerization, and version control 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 Babel Street office. Your technical prowess, including programming and ML 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 Babel Street.

Quick Tips For Babel Street Machine Learning Engineer Interviews

• Master the Basics: Ensure you have a strong grasp of machine learning fundamentals, including common algorithms and neural network architectures. Babel Street’s interview requires a solid understanding of both classical and modern ML techniques.

• Practical Knowledge: Babel Street places a significant emphasis on practical application. Be prepared to discuss how you’ve implemented models in a production environment and solved real-world problems.

• Collaborate and Communicate: Given the collaborative nature of the role, demonstrating excellent communication and collaboration skills can set you apart. Be ready to discuss past experiences where you worked effectively in a team setting.

Typically, interviews at Babel Street 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: What's the time complexity?

• Write 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. Complexity of (O(n)) required.

• Write a function precision_recall 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. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. 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 the 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 the number of job applicants decrease while job postings remain the same? You observe that the number of job postings per day has remained constant, 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 problems in "messy" datasets.

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

• How do you write a function to calculate sample variance? Write a function that outputs the sample variance given a 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 the A/B test results? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• How do you find the median 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. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks and common problems in 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 seen 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 forest. Additionally, discuss why one might choose random forest over logistic regression for certain problems.

• 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 track the model's accuracy and validity?

What is the average salary for a Machine Learning Engineer at Babel Street?

Q: What does Babel Street do? Babel Street illuminates identity and information for a safer, more productive world. Our proven AI-powered products transform data into knowledge, enabling users to build comprehensive pictures around identity and discover digital evidence. Our advanced data analytics and intelligence platform helps teams turn vast amounts of global, multilingual data into actionable insights, aiding high-stakes industries such as financial services, healthcare, and law enforcement.

Q: What are the responsibilities of a Machine Learning Engineer at Babel Street? As a Machine Learning Engineer at Babel Street, you will: - Optimize models and inference pipelines for performance and scale. - Develop deployable containerized ML services. - Create tools and practices for versioning, validating, deploying, scaling, and monitoring ML services. - Collaborate with the DevOps team to deploy and maintain models and services in production.

Q: What qualifications are required for the Machine Learning Engineer position? To qualify for the position, you should have: - Proficiency in Python and at least one other programming language such as Java or C++. - Experience with cloud infrastructure services such as AWS, Azure, or Google Cloud Platform. - Familiarity with containerization and orchestration technologies like Docker and Kubernetes. - Strong problem-solving skills and attention to detail. - Excellent communication and collaboration skills. - A Bachelor's or Master's degree in computer science, mathematics, statistics, or a related field. - 5+ years in software engineering experience and 3+ years in machine learning and neural networks.

Q: What benefits does Babel Street offer? Babel Street offers a comprehensive benefits package that includes: - Health benefits covering 90-100% of monthly premiums for Medical, Dental, Vision, Life & Disability insurances, for you and your family. - Competitive retirement plans including both a Traditional and Roth 401(K) with company match. - Unlimited Flexible Leave, trusting employees to manage their own time. - 12 paid Federal Holidays per year. - Tuition Reimbursement for continuing education.

Q: How can I prepare for an interview at Babel Street? To prepare for an interview at Babel Street, research the company's products and mission, practice common interview questions, and review your technical skills. Make use of Interview Query to gain insights into the types of questions asked and to practice with examples relevant to the Machine Learning Engineer role.

If you're excited about the opportunity to transform data into actionable insights and have a passion for leveraging machine learning to tackle real-world challenges, the Machine Learning Engineer position at Babel Street could be your perfect match. This role offers an excellent blend of cutting-edge technologies, dynamic collaboration, and impactful work. To get a deeper insight into what it takes to ace the interview and land this job, explore our detailed Babel Street Interview Guide. At Interview Query, we provide the ultimate toolkit to boost your confidence and readiness for any interview. Check out our company interview guides for more in-depth preparation, and feel free to reach out if you have any questions. Good luck with your interview journey at Babel Street!