PDT Partners is a research-driven quantitative asset management firm renowned for its use of cutting-edge technology and machine learning to make informed, analytical investment decisions. The firm fosters a culture of innovation and collaboration, striving to remain a leader in quantitative finance.

As a Machine Learning Engineer at PDT Partners, you are expected to dive deep into advanced machine learning methodologies to develop models that support the firm’s investment strategies. This role demands a high level of expertise in data analysis, algorithm development, and software engineering.

If you're looking to join a pioneering firm at the intersection of finance and technology, this guide is for you. We’ll walk you through the interview process, commonly asked questions, and provide valuable tips to help you succeed. Let’s get started!

Submitting Your Application

To begin your journey towards becoming a Machine Learning Engineer at PDT Partners, it's essential to submit a compelling application that highlights your technical skills and genuine interest in the position. Whether you've been approached by a recruiter or applied independently, meticulously review the job description and tailor your resume to align with the prerequisites.

Customize your resume by incorporating specific keywords that the hiring managers might look for and craft a targeted cover letter. Be sure to emphasize relevant skills and work experiences.

Recruiter/Hiring Manager Call Screening

If your resume makes it to the shortlist, a recruiter from PDT Partners' Talent Acquisition Team will reach out to verify your experiences and skill level. Behavioral questions might be a part of this initial screening.

In some cases, the hiring manager for the Machine Learning Engineer role may join the call to answer any questions you have about the role and the company. Surface-level technical and behavioral discussions could also take place during this stage.

This call typically lasts around 30 minutes.

Technical Virtual Interview

Clearing the recruiter round will earn you an invitation to the technical virtual interview. Typically conducted via video conference and screen sharing, this 1-hour interview will focus on various technical aspects relevant to the Machine Learning Engineer role at PDT Partners.

Expect questions on:

• Data systems
• ETL pipelines
• SQL queries
• Machine learning algorithms
• Hypothesis testing and probability distributions

For senior roles, you might be given case studies or real-scenario problems to solve.

Onsite Interview Rounds

After another recruiter call outlining the next steps, you'll be invited for an onsite interview loop at PDT Partners. Multiple rounds, varying by role, will be conducted throughout the day.

During the onsite interviews, your technical skills in programming and machine learning modeling will be evaluated thoroughly. If you were given a take-home assignment, you might need to present it during one of the onsite rounds.

Quick Tips For PDT Partners Machine Learning Engineer Interviews

• Brush Up on Core Concepts: Make sure you're well-versed in machine learning algorithms, data systems, and SQL queries. Practice solving problems that test these skills.
• Prepare for Behavioral Questions: Understand PDT Partners' core values and be ready to discuss how your experiences align with these values. Use the STAR (Situation, Task, Action, Result) method for structuring your responses.
• Practice, Practice, Practice: Utilize platforms like Interview Query to go through as many practice interview questions and mock interviews as possible.

For more insights and tips, check out Interview Query.

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

• 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 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 perm_palindrome to determine if a permutation of a string can be a palindrome. Given a string str, write a function perm_palindrome to determine whether there exists a permutation of str that is a palindrome.

• Write a function to return any subset of numbers that sum to zero without containing the number zero. You are given a list of integers called numbers. Write a function to return any subset of numbers where the elements sum to zero and that does not contain the number 0. If there are no combinations of elements that sum to zero, return an empty list.

• Write a function to sum two strings representing numbers without converting them to integers. Given two strings, num_str1 and num_str2, write a function to sum the two strings together without directly converting them to integers. Return the output in string format.

• What metrics would you look at to determine the demand for rides at any point? As a data scientist in a ride-sharing marketplace, identify metrics to gauge ride demand at any given time. Also, determine metrics indicating high demand and low supply, and establish a threshold for excessive demand.

• What testing strategies and metrics would you use to determine the success of Facebook stories without using a standard A/B test? You are tasked with measuring the success of Facebook stories. Explore alternative testing strategies and metrics to evaluate the feature's success, given that a standard A/B test is not an option.

• What metrics would you use to rank each Twitter user in influence? Given 100 Twitter users, identify the metrics to rank each user by influence. Explain how you would quantify a Twitter user's influence.

• What does the distribution of time spent per day on Facebook look like? Describe the distribution of time spent per day on Facebook. What metrics would you use to characterize this distribution?

• Show that if (f_X) is a strictly decreasing function, then (m \geq \mu). Given a continuous random variable (X) with probability density function (f_X(x)), mean (\mu), and median (m), demonstrate that if (f_X) is strictly decreasing, then (m \geq \mu).

• What is the probability of getting a pair in a hand of (N) cards from a 52-card deck? If you draw (N) cards without replacement from a standard 52-card poker deck, compute the probability of getting a pair (two cards of the same rank).

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and explain the tradeoffs between using a bagging algorithm and a boosting algorithm. Provide examples to illustrate the differences.

• How would you design an ML system to extract, transform, and store data from APIs for downstream models? As a machine learning engineer for a large bank, you have access to Reddit and Bloomberg APIs. Design a system that extracts data from these APIs, transforms it, and stores it in a format usable by downstream modeling teams for various applications like risk assessment, credit decisions, and marketing.

If you're eager to gain deeper insights into the company, explore our comprehensive PDT Partners Interview Guide, which delves into various interview questions you might encounter. Moreover, we have meticulously crafted interview guides for other roles, such as software engineer and data analyst, to help you navigate PDT Partners’ interview processes 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 Machine Learning Engineer interview question and challenge at PDT Partners.

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!