Meta Research Scientist, GenAI - Multimodal Audio (Speech, Sound, and Music) Interview Process

The interview process for the Meta Research Scientist, GenAI - Multimodal Audio (Speech, Sound, and Music) position is designed to evaluate your deep knowledge of multimodal AI, particularly in audio processing, speech recognition, and generative AI techniques. As someone who has gone through the interview process for this role, I’ll break down each stage of the process, the types of questions you can expect, and provide helpful tips to ensure you succeed.

1. Application & Initial Screening

The application process begins with submitting your resume and cover letter. For this role, Meta is looking for:

• Research expertise in audio processing, speech recognition, and generative AI: Highlight any experience you have working with audio data (speech, sound, and music) and how you’ve applied machine learning or deep learning in these areas.
• PhD research: Be sure to showcase your experience with multimodal AI, specifically if your research involves the interaction of audio with text, vision, or other modalities.
• Publications: If you’ve published papers in relevant areas such as speech synthesis, audio generation, or multimodal models in conferences like NeurIPS, ICLR, ICASSP, ISMIR, or CVPR, make sure to include these in your application.
• Technical skills: Emphasize your proficiency in programming languages such as Python, and your experience with libraries such as TensorFlow, PyTorch, HuggingFace, and any audio-specific libraries such as Librosa, DeepSpeech, or SpeechBrain.

Once your application is reviewed, you’ll likely be contacted by a recruiter for an initial screening call.

2. Recruiter Screening Call

The recruiter screening typically lasts around 30-45 minutes and serves to gauge your background, technical knowledge, and fit for the role. During this call, you can expect questions such as:

• Research background: “Can you briefly describe your PhD research and how it relates to multimodal audio, speech recognition, or generative AI?”
• Motivation: “Why are you interested in working at Meta, specifically in the GenAI Multimodal Audio team?”
• Technical expertise: “What audio processing techniques or models have you worked with? Can you describe how you’ve applied deep learning to audio data (speech, music, sound)?”
• Interest in Meta’s work: “What excites you about the intersection of speech, sound, and music in generative AI?”

The recruiter will evaluate your fit for the role and your interest in Meta’s research goals. If successful, you’ll be invited to the next stage, which usually involves technical interviews.

3. Technical Interview: Research Deep Dive

The technical interview usually lasts 60-90 minutes and focuses on understanding your research experience, problem-solving approach, and theoretical knowledge in multimodal AI, especially in audio and speech-related tasks. Expect questions like:

Research Deep Dive:

• “Tell me about your research on generative models for speech synthesis or audio generation. How did you tackle challenges related to audio realism or naturalness?”
• “How do you approach the alignment of audio and text in multimodal models like audio captioning or speech-to-text tasks?”
• “What challenges did you face in working with music generation or audio classification, and how did you overcome them?”

In this section, the interviewer is assessing your ability to articulate your research clearly and discuss specific challenges you encountered in working with audio data and generative models.

Multimodal AI and Audio:

• “How would you approach the design of a generative AI model that synthesizes speech and music simultaneously, based on an input text description?”
• “Can you explain how transformers or variational autoencoders (VAEs) are applied in generative audio tasks? How would you adapt them to handle multimodal inputs like text and sound?”

These questions will test your understanding of multimodal AI, and your ability to combine speech, sound, and text in a cohesive model. Be prepared to discuss the advantages and challenges of working with multimodal systems.

Evaluation Metrics:

• “How do you evaluate the performance of a generative speech model? What metrics would you use to assess speech synthesis quality, intonation, or prosody?”
• “For a multimodal music generation model, what evaluation methods would you use to measure both audio quality and relevance to input text?”

In these questions, Meta will assess your ability to design effective evaluation metrics for generative models and multimodal tasks.

4. Coding Challenge

In some cases, there will be a coding interview where you will be asked to implement a model or algorithm related to audio generation or multimodal AI. Here are some possible coding challenges you may encounter:

• Model implementation: “Write code to implement a waveform generation model (e.g., WaveNet) to synthesize speech or music based on an input sequence.”
• Data preprocessing: “Write a script to preprocess audio data for a speech synthesis task. Include steps for normalization, feature extraction, and data augmentation.”
• Model evaluation: “Write a function to evaluate a speech-to-text system’s performance using word error rate (WER) or character error rate (CER).”

You’ll likely be asked to use Python, PyTorch, or TensorFlow. Be comfortable with working with audio data, feature extraction, and evaluating generative models.

5. Behavioral Interview

The behavioral interview assesses your ability to collaborate with cross-functional teams, handle feedback, and work effectively in Meta’s research culture. Typical questions might include:

• Collaboration: “Tell me about a time when you worked with a team of engineers or researchers from other domains (e.g., computer vision, NLP) to solve a complex problem. How did you integrate different expertise?”
• Problem-solving: “Describe a situation when you had to overcome a major obstacle in your research. What was your approach to solving it?”
• Handling feedback: “How do you typically respond to critical feedback on your research? Can you share an example where feedback significantly improved your work?”

Meta places high value on teamwork, adaptability, and the ability to take constructive feedback in research environments.

6. Final Round with Senior Researchers

The final round typically involves interviews with senior researchers or leadership. This round focuses on evaluating your long-term research vision and how you fit within Meta’s strategic goals. Some sample questions might include:

• Research Vision: “Where do you see the future of generative AI for audio and multimodal models? How do you think your research can shape this future?”
• Meta’s mission: “Meta’s mission includes building the metaverse. How do you see generative AI for audio playing a role in this vision, especially in the context of virtual reality (VR) or augmented reality (AR)?”
• Cultural fit: “Meta values collaboration, transparency, and innovation. How do you foster these values in your own research environment?”

This is an opportunity to demonstrate your long-term vision and alignment with Meta’s goals, particularly in relation to the use of generative AI in multimodal audio.

7. Offer & Compensation

If you pass all interview stages, you will receive an offer. Meta Research Scientist Interns are typically compensated with:

• Hourly rate: Typically ranging from $40 to $60 per hour, depending on your experience and location.
• Stock options: Meta often includes equity as part of the compensation package.
• Benefits: Health insurance, paid time off, and access to Meta’s research resources, mentorship programs, and learning opportunities.

Tips for Success

• Review generative models: Brush up on WaveNet, GANs, VAEs, and transformers, particularly how they can be applied to audio generation, synthesis, and multimodal tasks.
• Understand multimodal learning: Be prepared to discuss how to integrate audio with text (e.g., for speech-to-text or audio captioning) and how to handle the challenges of aligning these modalities.
• Prepare for coding: Be ready to code in Python, PyTorch, and TensorFlow. Focus on tasks like speech synthesis, feature extraction, and model evaluation for generative tasks.
• Collaborate effectively: Meta highly values collaboration. Be prepared to discuss how you’ve worked in multidisciplinary teams and how your work can contribute to collaborative research.