Meta Research Scientist Intern, Embodied AI (PhD) Interview Process

The interview process for a Meta Research Scientist Intern, Embodied AI (PhD) position is rigorous and focuses on evaluating your technical expertise, research experience, and collaborative abilities. As someone who has interviewed for this role, I’ll provide you with a comprehensive overview of the process, common questions you can expect, and tips to succeed.

1. Application & Initial Screening

The process starts with submitting your resume and cover letter, where you should highlight:

• PhD research: Emphasize your experience in embodied AI, robotics, reinforcement learning, machine learning, computer vision, or multi-agent systems.
• Publications: If you have published in top conferences (e.g., NeurIPS, ICRA, CVPR, ICML, or ICLR), highlight them.
• Programming and technical skills: Proficiency in Python, TensorFlow, PyTorch, ROS (Robot Operating System), and libraries related to robotics or multi-agent systems.
• Problem-solving: Showcase any experience where you have addressed complex challenges related to AI, motion planning, or autonomous agents.

Once you submit your application, the recruiter will review your qualifications. If they find a good match, you’ll be scheduled for an initial screening call.

2. Recruiter Screening Call

The initial recruiter screening is typically 30-45 minutes long and focuses on understanding your background and assessing your fit for the role. The recruiter will ask:

• Research Background: “Can you describe your PhD research? How does it relate to embodied AI or reinforcement learning in robotics?”
• Motivation: “Why do you want to work as a research scientist intern at Meta, and what excites you about embodied AI?”
• Technical expertise: “What machine learning techniques have you applied in your research, especially in robotics or multi-agent systems?”
• Alignment with Meta’s goals: “Meta is focused on AI research that impacts the real world. How do you see your work contributing to Meta’s research in embodied AI?”

This interview serves to verify that your academic background and research interests align with the work being done at Meta. If successful, you’ll be scheduled for the next round.

3. Technical Interview: Research and Problem Solving

The technical interview is usually conducted by a senior researcher or a member of Meta’s AI team and lasts around 60-90 minutes. This interview focuses on your research expertise, ability to solve complex problems, and approach to embodied AI. Here’s what to expect:

Research Deep Dive:

• “Can you explain the key challenges you faced in your PhD research? How did your research contribute to the field of embodied AI or robotics?”
• “What techniques did you use in robot perception, and how can they be applied to real-world robotic applications?”
• “Can you describe an example where you solved a complex problem related to motion planning, autonomous agents, or robot interaction?”

During this part of the interview, you will need to articulate the impact and real-world applications of your research. Be ready to explain how your research could advance the field of embodied AI at Meta.

Technical Problem-Solving:

• “Given a robot navigating in a complex environment, how would you approach the problem of collision avoidance in real time?”
• “Explain how you would design a reinforcement learning model for a robot to learn a new task, such as picking up objects or moving in a cluttered environment.”
• “What are the key challenges in multi-agent systems for robotics? How would you ensure effective communication and coordination between agents?”

Here, the interviewer will assess your ability to approach complex technical problems related to robot behavior, motion planning, decision-making, and real-time processing.

AI Techniques & Tools:

• “How do you integrate reinforcement learning with robot perception and action planning? Can you explain the feedback loop?”
• “What are the key differences between traditional control systems and AI-based decision-making in robotics? How do they complement each other?”

4. Coding Interview

In this round, you may be asked to solve coding challenges that test your ability to implement algorithms or work with data related to AI, robotics, or computer vision. The coding interview may involve:

• Reinforcement learning: “Write code to implement a Q-learning agent that learns to navigate a maze or simulate a robot task.”
• Perception and computer vision: “Given an input stream of images from a camera, how would you extract relevant features (e.g., keypoints, depth maps) for robot navigation?”
• Motion Planning: “Write a function to compute the path planning for a robot in a dynamic environment using A* search or RRT (Rapidly-exploring Random Tree).”

You may be asked to code in Python, C++, or TensorFlow/PyTorch. Be ready to discuss algorithmic efficiency and potential optimizations.

5. Behavioral Interview

In this round, Meta will assess how well you fit into their collaborative and fast-paced culture. They are interested in understanding how you work with cross-functional teams, handle feedback, and contribute to team-based problem solving. Expect questions like:

• Team Collaboration: “Tell me about a time when you worked with a multi-disciplinary team (e.g., engineers, designers, or other researchers). How did you collaborate to solve a challenging problem?”
• Problem-solving: “Can you describe a situation where you had to adapt your research strategy in response to unexpected results or setbacks?”
• Feedback handling: “What is the most significant feedback you’ve received during your research, and how did it change the direction of your work?”

Meta values collaboration and feedback, so be prepared to share specific examples that demonstrate your ability to work in teams, take initiative, and learn from experience.

6. Final Round: Leadership and Research Vision

The final round is typically with senior researchers or leadership and focuses on your vision for embodied AI and your alignment with Meta’s research goals. Example questions include:

• Research Vision: “Where do you see embodied AI evolving in the next 5 years? What role do you see Meta playing in this evolution?”
• Cultural Fit: “Meta values openness, collaboration, and transparency. How do you ensure these principles are reflected in your research and teamwork?”
• Long-term Contributions: “What specific contribution do you hope to make at Meta? How do you envision your work impacting the development of Meta’s AI products?”

This round is your opportunity to show your long-term potential at Meta and how your research vision aligns with Meta’s goals in AI and robotics.

7. Offer & Compensation

If you are successful in all rounds, you will receive an offer. Compensation for Meta Research Scientist Interns generally includes:

• Hourly rate: Depending on your experience, this can range from $40 to $60 per hour.
• Stock options: Meta typically offers equity as part of the compensation package.
• Benefits: Paid time off, health insurance, and access to Meta’s mentorship programs and research resources.

Tips for Success

• Master the fundamentals: Review key concepts in reinforcement learning, robot perception, motion planning, and multi-agent systems.
• Be prepared for coding challenges: Practice coding problems related to reinforcement learning, robotic control systems, and AI algorithms. Platforms like LeetCode, HackerRank, and PyTorch tutorials are great places to start.
• Know the latest in embodied AI: Stay up-to-publishDate with the latest research papers and breakthroughs in embodied AI, robotics, and multi-modal learning.
• Showcase your research impact: Be ready to explain how your research fits into the broader context of AI, robotics, and Meta’s vision for the future of technology.
• Highlight collaboration skills: Meta places a high emphasis on cross-functional teamwork, so prepare examples that showcase how you work with others, whether it’s in a lab setting or a collaborative research environment.