Verified by ML Engineering Manager at Meta

Meta ML Engineering Manager Interview

Interview Guide Apr 01

The role of a Meta ML Engineering Manager

Meta actively seeks out the world's most ingenious and seasoned engineering leaders. Meta's ML engineering managers are architects of high-performing teams, crafting an environment where engineers experience the pinnacle of their career impact. 

Guiding their teams with vision and fostering professional growth, Meta's Machine Learning Engineering Managers steer diverse endeavors within the engineering divisions. This includes managing data infrastructure, a cornerstone at Meta, and driving product engineering to deliver a multitude of products to Meta's vast community of 2 billion people.

The role requires Meta Machine Learning Engineering Managers to seamlessly blend progressive machine learning expertise with managerial acumen. The average total compensation for Meta's Engineering Managers is an impressive $405,000, comprising:

  • Base Salary: $238,167
  • Stock Grant (per year): $143,667
  • Bonus: $23,167

Phone Screening with a Recruiter


In the initial phase, you'll have a phone screening with one of Meta's recruiters. This is basically an icebreaker to get to know you better and understand your background. Don't worry, it's pretty straightforward and not too time-consuming.

Following this, you'll have a prep call with the recruiter, where they'll guide you through what's to come in the subsequent rounds.

Interview Questions

  • Can you walk me through your background and experience?
  • What interests you about the ML Engineering Manager role at Meta?
  • Have you managed teams or projects before? Could you share a specific example?
  • How do you handle challenges in a team environment?

Interview with an Engineering Manager


This segment involves a 45-60 minute phone call with an EngineeringManager. The interview itself comes in two parts: First, you'll dive into people management and leadership topics. Then, it's onto the technical side, where system design takes the spotlight (expect a mix of technical discussions related to your background).

After this, the recruiter will schedule another call to get you prepped for the main interview rounds. What's noteworthy about Meta is that they keep you in the loop throughout the process, and provide detailed feedback—which can be a real eye-opener.

Interview Questions

You can expect questions like:

People Management and Leadership:

  • Describe a situation where you had to resolve a conflict within your team.
  • How do you motivate and inspire your team members?
  • Can you share an example of a successful team collaboration you led?
  • Tell me about a time you had to make a tough decision that impacted your team.
  • How do you handle performance issues or challenges with team members?

Technical and System Design:

  • Can you explain a complex technical concept to a non-technical audience?
  • Walk me through the architecture of a project you've worked on.
  • How would you design a scalable system for [specific scenario]?
  • Share an experience where you optimized a system for better performance.
  • What's your approach to incorporating new technologies into existing systems?



The onsite interviews are where the real challenge lies for aspiring Meta ML Engineering Managers. It's where the real assessment happens. Brace yourself for a series of five or six separate 45-minute interviews-you'll meet different interviewers from Meta, and they'll cover a range of topics which include:

  • 1 or 2 System Design Interviews
  • Project Retrospective Interview
  • People Management or Technical Project Deepdive Interview
  • Machine Learning
  • 1 or 2 Coding Interviews
  • Culture-fit Interview

Let's discuss each of these in detail:

System Design Interviews

You will face 1 or more system design interviews where the focus is on your prowess in crafting and structuring scalable systems. You'll be tasked with demonstrating your understanding of complex architectural concepts and your ability to design solutions that can handle massive loads. 

Project Retrospective Interview

In this round, the spotlight is on your previous project experiences and problem-solving capabilities. You'll be asked to provide in-depth insights into specific projects you've worked on. This interview provides an opportunity to demonstrate your experience, decisions, and the outcomes of the projects you've been involved with.

People Management Interview

In this segment, your leadership skills and people management abilities take center stage. You'll be evaluated on your approach to handling various managerial scenarios.

Culture-fit Interview

This round aims to assess your alignment with Meta's work culture and values. Expect discussions around your personal and professional experiences, your motivations, and how well you jive with the company's ethos.

Machine Learning

The machine learning (ML) practical design interview revolves around your ability to build ML systems at scale. 

Your interviewer might dive into specific types of systems like ads or distributed learning systems, or they might take a more product-focused approach. The good news is they don't expect you to talk about complex algorithms from memory, like quad trees or Paxos.

Coding Interviews

Here's where your technical skills take the spotlight. You'll face one or two coding interviews, which are customized to your specific role. These interviews gauge your problem-solving abilities, coding proficiency, and your capacity to think critically when approaching technical challenges.


Here are a few tips to prepare for this round:

  • Pick an app and select a system within it that could benefit from ML. Imagine it's currently using a few rules for a small user base, and your task is to revamp it using ML for widespread impact. Your design should cover key components like problem formulation, optimization function, supervision signal, feature engineering, data source, representation, model architecture, and evaluation metrics.
  • Practice turning intuitive ideas into concrete features – For instance, if you're thinking about 'likes,' delve into normalization, smoothing, and bucketing for a more robust approach.
  • Consider the problem end to end. What if the model tanks after training? How do you debug it? How do you evaluate and continually deploy it? Work through these problems on paper, break them down, and check out common large-scale systems like Memcached or dive into how search engines operate. But remember, during the interview, don't just regurgitate what you've read – tailor your solution to the specific question.
  • Be ready to analyze your approach and have a toolkit of various algorithms with an understanding of their trade-offs. For instance, make sure you are able to discuss the advantages of logistic regression compared to SVM.

Interview Questions

System Design

  • Explain the bottleneck of the system.
  • Architect a drive-through system.
  • Design an app for video upload and sharing.
  • Design the architecture of the MVP for a restaurant recommendation service.
  • Design an app for video upload and sharing.
  • Outline the MVP architecture for a restaurant recommendation service.
  • Present a technical solution to an engineering problem using diagrams.

Project Retrospective

  • What's your approach to 1-on-1 meetings? How does your approach differ between individual contributors and managers?
  • Describe the most technically intricate project you've overseen. What challenges did you face, and how did you resource your team?
  • How do you strike a balance between autonomy and direction? Provide an example of leading the team in a different direction than their initial preference.
  • Describe something you have achieved and how you have done it.
  • Tell me about a time you scaled a system
  • Reflect on a mistake you made and the lesson you learned from it.
  • Detail your recent year's work highlights.

People Management

  • How do you measure the success of the engineering team you are managing?
  • What's your approach to 1-on-1s? How does your discussion change between ICs and managers?
  • How do you manage your team’s career growth?
  • How do you manage difficult conversations?
  • How do you manage underperforming employees?
  • Tell me about a difficult employee situation that you handled well/not so well.
  • What would you do with someone that had stayed at the same level for too long?
  • How do you recruit good engineers?
  • How do you manage projects?
  • Describe a tough situation where you demonstrated leadership.
  • A senior leader from a different org has challenged your approach to a specific problem your team is working on, in a weekly standup. What would you do next?
  • How do you create alignment between talented individuals that also happen to be strongly opinionated?


  • Tell me about what you've been working on over the last year.
  • Tell me about yourself.
  • Why are you leaving your current job?
  • How do you communicate about technical project needs with non-technical teams?
  • Tell me about a mistake you made and the lesson you learned from it.

Machine Learning

  • Can you grasp the entirety of the problem and its solution space?
  • Are you skilled in feature engineering?
  • Can you identify flaws in ML systems and propose enhancements?
  • Can you design reliable evaluation and deployment methods?
  • Are you familiar with the architecture requirements (storage, performance, etc.) of your system?
  • Can you integrate product requirements effectively into your ML system?


  • Design a key value store.
  • Design Google search.
  • Architect a worldwide video distribution system.
  • Build Meta Chat.


  • Given a collection of intervals, merge any overlapping intervals.
  • Find the length of the longest substring of a given string without repeating characters.
  • Evaluate the value of an arithmetic expression in Reverse Polish Notation.
  • Given a reference of a node in a connected, undirected graph, return a deep copy of the graph.
  • Given inorder and postorder traversal of a tree, construct the binary tree

6 Tips to Prepare for the Meta ML Engineering Manager Interview

  1. Make sure you're well-versed in architectural concepts and can design solutions that handle heavy workloads. So, if you're asked to design a real-time messaging system for a social media platform with millions of active users. Discuss how you'd architect the system to handle high concurrency, ensure data consistency, and provide low-latency communication. Use diagrams to illustrate components like message queues, databases, and load balancers. Lastly, justify your design choices, such as the selection of specific technologies or trade-offs between performance and cost.
  2. The project retrospective interview is your chance to showcase your project leadership and problem-solving abilities. Be ready to share the nitty-gritty details of your previous projects, from challenges to outcomes. Paint a clear picture of your decision-making process, how you collaborate, adapt, enable teamwork, etc.
  3. During the people management interview, focus on your leadership abilities and how you handle various managerial scenarios. Share instances where you've resolved conflicts, nurtured team growth, and provided guidance. Convey your commitment to supporting and motivating your team.
  4. The culture-fit interview gauges how well you align with Meta's values. Share personal and professional experiences that showcase your compatibility with Meta's work culture. Emphasize your adaptability, teamwork, and innovation in a dynamic environment.
  5. For the coding round, make sure you walk them through your step-by-step approach to optimizing the algorithm's time and space complexity. Provide a clean and well-structured code implementation in your preferred programming language. As you explain your code, articulate your thought process, highlighting the trade-offs you made for efficiency. Make sure you're solid on basic ML theory, especially concepts like overfitting and regularization.
  6. One valuable tip is practicing with mock interviewers or Meta ML Engineering Managers (you can find several on Prepfully). This will help you replicate the pressure and dynamics of an actual interview, so you can practice responding confidently and demonstrating your skills effectively. You will also get detailed feedback after each practice session to identify areas for improvement and refine your performance. Book a one-on-one session with a Meta EM on Prepfully now.

Meta ML Engineering Manager Roles and Responsibilities

Following are the roles and responsibilities of a Meta ML Engineering Manager:

  • As a Meta ML Engineering Manager, you'll wear two hats - being highly technical and an effective people manager. This means not only having a deep understanding of machine learning but also being able to lead and guide your team effectively.
  • You're expected to be a subject matter expert in machine learning within a specific product domain. You'll lead teams working on multiple projects with increasing dependencies. Often, these projects might be in areas that are either ambiguous or carry a high-impact, which makes your role all the more critical.
  • Working closely with your team and cross-functional partners, you'll help shape and influence the overall strategy. This includes contributing to roadmap creation and ensuring its successful execution.
  • It's not just about managing projects - you'll collaborate with various teams, drive engineering initiatives, and make an impact at the organizational level.
  • Don't be surprised if you find yourself participating in technical design discussions. Your insights are invaluable in ensuring that projects are set up for success from the get-go.
  • You'll need to measure the impact of your team's work, and this involves setting clear expectations and goals.

Meta ML Engineering Manager Skills and Qualifications

Here are the skills and qualifications that a Meta ML Engineering Manager must have:

  1. As a Meta ML Engineering Manager, you should have a solid foundation with at least 5+ years of hands-on experience in software engineering. An advanced degree (Master's or PhD) in a relevant field such as computer science, machine learning, or a related discipline can be advantageous.
  2. You should be comfortable with at least one of the core languages commonly used in machine learning, such as Python and TensorFlow/PyTorch.
  3. Having 5+ years of technical management experience is a must. You should be adept at steering projects, ensuring effective API design, and managing the dynamic interactions between servers and clients.
  4. Demonstrated experience in recruiting technical teams is a key aspect. You should have a proven track record in leading and managing machine learning engineering teams.
  5. While a BA/BS in Computer Science is preferred, having substantial work experience (4+ years) in lieu of a degree is also acceptable. Your hands-on experience should ideally showcase your technical prowess and leadership capabilities.