Meta ML Engineering Manager Interview

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.

• 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?

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.

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.

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?

Culture-fit

• 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

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

Coding

• 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