Interview Experience for Applied Research Scientist, Instagram Core ML (PhD) at Meta

I recently interviewed for the Applied Research Scientist, Instagram Core ML (PhD) position at Meta and want to share a comprehensive account of my experience. This role is centered around cutting-edge machine learning research, with a focus on recommendation systems, deep learning, and generative models. Below is a detailed breakdown of the interview process, questions I encountered, and tips to help you prepare if you’re applying for this role.

Interview Process Overview

The interview process for the Applied Research Scientist, Instagram Core ML (PhD) position was highly rigorous and structured to test both technical depth and research expertise. The process included several stages:

1. Recruiter Call

The process began with an introductory call from a recruiter. This was a relatively straightforward conversation where the recruiter reviewed my resume and asked about my background. They were particularly interested in:

• My PhD research and its relevance to machine learning and AI.
• My experience with deep learning, reinforcement learning, recommendation systems, and NLP.
• My motivation for applying to Meta and working with Instagram’s Core ML team.

The recruiter also provided an overview of the role and clarified key responsibilities, such as improving Instagram’s recommendation algorithms using machine learning models and generative models. They also asked about my experience with PyTorch, TensorFlow, and large-scale data handling, which are crucial for the role.

2. Technical Screening

The technical screening involved solving a series of coding and machine learning-related problems:

• Coding Challenge: I received a timed coding challenge focused on algorithm design and data structures. The problems required a solid understanding of dynamic programming, greedy algorithms, and graph algorithms. For instance, I was tasked with solving a shortest path problem on a large graph using an efficient algorithm (Dijkstra’s or A*).

• Machine Learning Problem: I was given a real-world ML problem related to recommendation systems and asked to design a system. The problem required me to develop a recommendation engine based on user-item interactions, with the added challenge of considering the cold start problem and scalability for millions of users. They tested my knowledge of collaborative filtering, content-based filtering, and matrix factorization techniques.

3. Onsite Interviews (Virtual)

The onsite portion was virtual and divided into multiple technical and research-focused rounds. Each round was conducted by a different expert, and each had a specific focus:

• Machine Learning Algorithms and Theory: In this round, I was asked to dive deep into the theory behind machine learning models, particularly around deep learning and reinforcement learning. I was asked to explain how different algorithms work, and to provide examples of how they could be applied to improve Instagram’s recommendation models.

  Example Question: “Explain how you would use reinforcement learning to improve the recommendation system on Instagram. What challenges would you face in terms of exploration vs. exploitation?”

• System Design and Scalability: This was one of the most challenging parts of the interview. I was tasked with designing a scalable machine learning system capable of handling millions of users and items for a recommendation engine. They were looking for my ability to design distributed systems using modern architectures (e.g., GPU-based computing, cloud infrastructure, etc.).

  Example Question: “Design a system to deliver real-time personalized recommendations to Instagram users. How would you handle the trade-offs between latency and model accuracy? What infrastructure and algorithms would you use?”

• Research and Publications Discussion: Since the role was for a research scientist, I was asked in-depth questions about my PhD research and any publications I had. I had to explain my past work in machine learning, especially if it was related to generative models or recommendation systems. The interviewers also probed my ability to conduct independent research and contribute to advancing Core ML at Meta.

  Example Question: “Tell us about a research project you worked on involving generative adversarial networks (GANs). How would you adapt this research to improve Instagram’s content generation?”

• Behavioral and Leadership Interview: This interview focused on assessing my ability to work in cross-functional teams, mentor junior researchers, and manage large, complex projects. They asked about my experience working in a collaborative, fast-paced environment and how I’ve handled setbacks or failures in research.

  Example Question: “Describe a time when you had to lead a research project with tight deadlines. How did you manage the team, resources, and time?“

4. Final Evaluation and Offer

After completing the onsite interviews, I had a debrief with the hiring manager. This meeting was more about discussing the impact of my potential contributions to the team and aligning my research interests with Instagram’s current ML goals. They also discussed compensation and benefits.

Key Skills Tested

• Machine Learning Expertise: The interviews focused heavily on theoretical and applied machine learning, especially in the context of recommendation systems and generative models. I was expected to demonstrate knowledge in a wide array of ML topics like deep learning, reinforcement learning, collaborative filtering, and matrix factorization.

• System Design & Scalability: A significant portion of the interview tested my ability to design scalable ML systems. Given the scale at Instagram, they were particularly interested in how I would handle large datasets, real-time systems, and cloud computing.

• Research and Publications: Since this is a research scientist role, they placed a strong emphasis on my research experience. I was asked to discuss my past work in detail, especially anything related to deep learning or generative models. Having publications at top conferences like NeurIPS, ICML, or CVPR can be beneficial here.

• Technical Leadership: Leadership and mentorship were also tested. The interviewers wanted to know how I would contribute to the research culture at Meta and how I would handle the challenges of leading a team of researchers while collaborating with engineers and product managers.

Preparation Tips

• Review Core ML Concepts: Be sure to review key concepts in recommendation systems, generative models, and reinforcement learning. Also, brush up on neural networks, deep learning architectures, and optimization algorithms.

• Focus on System Design: Practice designing scalable ML systems and data pipelines. Understand how to leverage cloud technologies, distributed computing (GPUs, TPUs), and real-time data processing.

• Read and Present Your Research: Be ready to discuss your PhD research in depth. Be prepared to explain its relevance to Instagram’s goals and how it can be applied to improve Core ML. If you have relevant publications, make sure to bring them up and be ready to discuss their significance.

• Prepare for Behavioral Questions: Given that this is a leadership role, practice answers to behavioral questions. Think about examples where you’ve managed teams, led complex projects, and navigated challenges in research.

• Stay Updated on Instagram’s ML Work: Familiarize yourself with Instagram’s existing machine learning models, especially those used in recommendations, content generation, and image/video processing.