### 2025 interviews

##### Matthew - HR - 8/22/2025
- 7.5 years in Meta, product hiring 
- Primary HR POC going forward
- Next steps 
	- Product sense, analytical - not different as such, just a different question
	- IC or leader 
	- Frameworks
		- Use structure that suits the question
	- Team matching possibilities
	- Interview differentiators to stand out better 
- High priority AI ML roles
	- Business AI Agents
	- Business AI PM
	- Llama Evaluations Platform
	- LlamaX - Llama Stack
	- GenAI data PM, Agentic
	- GenAI Security Foundations PM
	- WhatsApp - AI Growth
- Feedback from first round - both interviews were great
	- Analytical thinking 
		- Solid overall thinking - 
		- Strength - growth levers, product stage, AI glasses vs pin tradeoffs - short & long term implication, industry trends,   
	- Product sense 
		- Went really well 
		- Strength - Users, journey, pain points, 
		- Improvement area - solution was non-scalable, convoluted, was focused on hardware, MVP could've been more feasible solution, would've liked to explore other possible solution spaces. For solution development, feel free to explore multiple potential solutions on high level, make it a collaborative process to figure out what's the most optimal solution. Hardware would be difficult to scale. 
	- Leadership & drive 
		- Driving resourcefulness and results - challenging project, proud achievement
			- Focus is on driving results, having broad impact on company, achieving multiple outcomes
		- Growing continuously - 
			- Candidates should be self-aware and want to seek growth and have plans to improve 
			- Your growth areas today, growth mindset type question
			- Critical feedback, how did you respond, how you took actions
		- Resolving conflict - 
			- Disagreements between cross functional partners on how to move forward with your project
			- Show you're comfortable to have a tough conversation, share diplomatically
			- Showing empathy 
		- Taking ownership and accountability 
			- A time you failed, what did you do? I messed up, and I learned a lot
			- Never say pointing fingers at someone
			- Leading ambiguous projects 
	- Scheduling 
	- Team matching 
		- Unless a team is matched, offer may not be extended
		- Can take 3+ months
		- HM shows interest and then I show interest to talk about team, role 
		- All new hires to be in office 3 days a week 
		- Product teams are mainly in Bay Area, Seattle, NY
		- 
##### Alexis - HR - 8/12/2025
- Feedback from screening rounds 
	- Analytical - strongest, short-long term impacts of AI pin over AI glasses were well laid out, industry trends, 
		- Strengths - product rationale 
		- Improvements - goal setting, did not talk about ML-specific metrics much 
	- Product sense - need to practice more, solution was a bit convoluted and wouldn't scale, need to think more about solution space, easiest solution in MVP could be more around software, 
		- Product motivation, target audience, pain points were strong 
	- 
- Next steps
	- Introductions, next round scheduling - 
	- Work on frameworks
	- Glassdoor for interview experiences 
	- Mock interview video  
- Team match possibilities to understand fit - not before offer, 




	[[Meta Product Manager]]
[[Meta Prep plan]]
### About Meta
##### Company mission: 
Build the future of human connection and the technology that makes it possible.
##### Rev breakdown
Ads contains revenue from Family of Apps - $160B in 2024
- Facebook
- Instagram
- Threads
- WhatsApp
Reality Labs / Hardware devices - $2B in 2024
- Quest headsets
- Meta RayBan AR glasses
- Metaverse

##### Ads revenue model
- Impression based ads - CPM cost per mile = cost per 1000 impressions
- Action based ads - CPC cost per click or CPA cost per acquisition
	- Clicks are clicking on ad. Useful for top of the funnel awareness and branding/visibility campaigns.
	- Acquisitions are completing a valuable activity like lead generation, demo sign-up, app download. Useful for mid or bottom of funnel campaigns when tracking user intent is critical for the advertiser business. 

##### Meta's 5 Strategic Priorities - announced in Q1CY24
Improved Advertising, 
Engaging Experiences, 
Business Messaging, 
Meta AI, 
AI Devices

##### Key competition
- Facebook - Reddit, Twitter, Nextdoor, TikTok, YouTube (News), Discord
- Instagram - TikTok, YouTube, Snapchat, Pinterest
- Threads - Twitter, Bluesky, Discord, Mastodon
- WhatsApp, FB Messenger - iMessage, Signal, WeChat, Telegram, Google Messages, Snapchat
- FB Marketplace - eBay, Nextdoor, Craigslist, OfferUp, 

| **Meta App**       | **Key Competitor(s)**               | **Meta Strengths**                                                                            | **Competitor Strengths**                                                                                                                                                                  | **Meta Weaknesses**                                                                                                                                                                                                                                 | **Competitor Weaknesses**                                                                     |
| ------------------ | ----------------------------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
| **Facebook**       | Reddit, Twitter (X), Nextdoor       | - Broad reach across age groups<br>- Strong groups + events infra<br>- Marketplace scale      | - Reddit: Stronger for niche/anonymous communities<br>- Twitter: real-time trends/news<br>- Nextdoor: hyperlocal utility                                                                  | - Aging user base<br>- Perceived as cluttered<br>- Trust/safety concerns                                                                                                                                                                            | - Reddit: difficult UX for new users<br>- Twitter: toxic discourse- Nextdoor: moderation issu |
| **Instagram**      | TikTok, YouTube Shorts, Snapchat    | - Large creator ecosystem<br>- Cross-Meta reach<br>- Better ad monetization                   | - TikTok: best-in-class algo<br>- YouTube: long-form + Shorts<br>- Snap: Gen Z loyalty                                                                                                    | - Losing culture relevance among teens<br>- Reels seen as TikTok copy                                                                                                                                                                               | - TikTok: geopolitical scrutiny<br>- YouTube: not mobile-first- Snap: poor monetizat          |
| **Threads**        | Twitter (X), Mastodon, Bluesky      | - Instagram integration<br>- Clean UX<br>- Real identity trust                                | - Twitter: dominant real-time discourse<br>- Mastodon: decentralized fans<br>- Bluesky: open protocol                                                                                     | - Low DAU retention<br>- Limited API and tools<br>- Not yet "default" for breaking news                                                                                                                                                             | - X: brand damage post-Musk<br>- Mastodon: confusing UX<br>- Bluesky: low user                |
| **WhatsApp**       | iMessage, Signal, Telegram          | - Global dominance<br>- End-to-end encryption<br>- Business API scale                         | - iMessage: default on iOS<br>- Telegram: features + communities<br>- Signal: privacy-first                                                                                               | - Weak adoption in US<br>- No native desktop iPad app<br>- Limited integrations vs Telegram                                                                                                                                                         | - iMessage: Apple-only<br>- Signal: fewer features<br>- Telegram: weaker E2E encryption for g |
| **Messenger**      | iMessage, Snapchat, Discord         | - Facebook integration<br>- Business messaging features<br>- Cross-platform                   | - iMessage: seamless UX on iOS<br>- Snap: Gen Z dominance<br>- Discord: vibrant communities                                                                                               | - Fading relevance with younger users<br>- Too tied to Facebook                                                                                                                                                                                     | - iMessage: Apple-only<br>- Snap: low session time per user<br>- Discord: messy onbo          |
| **FB Marketplace** | Craigslist, OfferUp, Nextdoor, eBay | - Social graph + trust signals<br>- Huge reach<br>- Native to Facebook app<br>- No commission | - Craigslist: simplicity, longevity<br>- OfferUp: better UX, mobile first<br>- Nextdoor: hyperlocal identity<br>- eBay: national/international reach, buyer protections, shipping support | - Trust/safety concerns<br>- Poor search/sorting/filtering<br>- Lacks transaction guara - Craigslist: outdated design<br>- OfferUp: less liquidity<br>- Nextdoor: limited buyer intent<br>- eBay: Fees, friction for casual sellers, complex llers, |                                                                                               |



### Customer segmentation
Segmentation techniques 
- Demographics - age, gender, location
- Interest based - page likes, posts, groups
- Behavioral (intent or actions based) targeting - browsing, past purchases, conversions
- Use all above to predict what users will do next

Advertizers have other options
- Custom audience list - bring your own audience list, Meta brings customer profile matches. 
	- Expand to lookalike customer profiles based on similarities
- Fine tuning advertizing - 
	- Operational segmentations - existing, engaged and new
	- Balance relevance with new audience

Facebook 
- Growth in older users for US/UK but youth dominates India market
Instagram
- 18-34 year old - over 60% 
- Every follow is a signal, 44% of users say they will purchase from a business they follow
- Huge for eCommerce - 
WhatsApp
- 2B globally, 600M in India
- Massive business-to-consumer channel, sending messages and personalized chat
Messenger 
- 40M businesses use it
- Facebook marketplace, focus on purchase intent
Threads
- Leveraging Instagram's audience to expand and segment users 

Segmentation could become more unified by bringing common backend architecture, following a user across different Meta apps, to further increase targeting effectiveness. 


# Job Roles
### Business AI platform 

##### Job description: 
We envision a world where AI agents are the primary way that businesses interact with customers, for everything from sales to customer support. We are building the leading business AI agent platform to power business <-> consumer interactions, spanning all channels (i.e., both Meta’s 1P channels and 3P channels such as webchat, SMS, and phone) and use cases (i.e. sales, support). This will unlock tremendous value for both businesses and users, and is one of the most important and exciting new investment areas across the company. 

This role 

We are looking for an IC6 PM to drive our Business AI Platform. In this role, you will be responsible for the AI Agent itself – what it says, how good it is at closing sales on behalf of the business, how good a brand representative it is – you will drive the core conversation experience and the capabilities provided to businesses to customize their agents. To do this, you’ll work with a world-class team on cutting-edge tech and be on the frontier of using GenAI to drive real business outcomes. If you are successful, you’ll have built a powerful platform to enable Business AI agents for any business vertical, use case, customer segment that changes the way businesses operate and drive $B of incremental revenue impact for Meta.

# Report on Meta 

Here's a detailed report to help you prepare for your interviews for the IC6 Principal Product Manager role for Meta's Business AI Platform.
## Driving the Future of Business: Meta's Business AI Platform and Strategic Moats

## I. Introduction: The Vision for Business AI at Meta

Meta is making a bold strategic bet on the future of business-to-consumer (B2C) interaction, envisioning a world where AI agents become the primary interface between businesses and their customers for everything from sales to customer support. As an IC6 Principal Product Manager for the Business AI Platform, you would be at the forefront of this transformation, responsible for the core conversation experience and the capabilities that enable businesses to customize these agents. This initiative is designed to unlock tremendous value for both businesses and users, driving billions of dollars in incremental revenue for Meta by spanning all channels (Meta's 1P and 3P like webchat, SMS, phone) and use cases (sales, support).

This report will delve into the diverse business verticals currently leveraging Meta's platforms, analyze how this new Business AI Platform will strategically benefit Meta, and examine the unique competitive advantages (moats) Meta can build against other large language model (LLM) providers in this rapidly evolving space.

## II. Business Verticals on Meta's Platforms

Meta's Family of Apps (Facebook, Instagram, WhatsApp, Messenger, Threads) serves a vast and diverse ecosystem of businesses, ranging from small and medium-sized enterprises (SMBs) to large corporations. In 2022, Meta reported over 10 million active advertisers across its platforms, with a significant portion being SMBs. These businesses span numerous industries, primarily leveraging Meta's platforms for advertising, customer engagement, and direct sales.   

Key business verticals that are highly active on Meta include:

- **E-commerce and Retail:** This is consistently highlighted as the largest contributor to Meta's year-over-year ad revenue growth. Businesses in this sector use Meta for product discovery, direct sales through features like Facebook Shops and Advantage+ Shopping Campaigns, and driving traffic to their online stores. A substantial portion of consumers, including 40% of Gen X and 38.5% of US adults, use Facebook for purchases.   
    
- **Hospitality:** This includes businesses like hotels, restaurants, and travel agencies, which utilize Meta for bookings, customer service, and promoting experiences.   
    
- **Financial Services:** Banks, insurance companies, and other financial institutions use Meta for lead generation, customer support, and building brand awareness.   
    
- **Local Businesses:** Restaurants, fitness centers, salons, and other local service providers rely on Meta for local advertising, customer communication, and community engagement.   
    
- **Gaming:** While gaming promotions from China-based advertisers saw a reduction in Q1 2025, it remains a significant vertical for ad spending.   
    
- **Other Verticals:** Meta's broad targeting capabilities based on demographics, interests, and behaviors allow a wide array of other industries to effectively reach their specific audiences. This includes sectors focused on brand building, lead generation, and direct sales across various product and service categories.   
    

The sheer scale of Meta's user base (3.98 billion monthly active people across its apps in January 2025) provides an unparalleled reach for businesses across these verticals, making Meta's platforms indispensable for their marketing and customer interaction strategies.   

## III. How the Business AI Platform Can Help Meta

The Business AI Platform is a strategic imperative for Meta, designed to significantly enhance its value proposition to businesses and unlock substantial new revenue streams. The job description explicitly states its goal to "unlock tremendous value for both businesses and users, and is one of the most important and exciting new investment areas across the company," with the potential to "drive $B of incremental revenue impact for Meta."

Here's how this platform directly benefits Meta:

1. **New Revenue Streams and Monetization:**
    
    - **Direct Agent Monetization:** Mark Zuckerberg anticipates that "just like every business has an email address, social media account, and website, they'll also have an AI business agent for customer support and sales".This implies a future where Meta can monetize the deployment and usage of these AI agents, potentially through subscription models, per-interaction fees, or commission on sales closed by agents. The WhatsApp Business Platform already charges businesses a fee for each message sent to customers.   
        
    - **Enhanced Ad Performance:** The AI agents can directly improve the effectiveness of Meta's core advertising business. By automating customer service and driving conversions directly within chat apps , these agents can increase the ROI for advertisers, leading to higher ad spend and better conversion rates for campaigns like "click-to-message" ads. Meta's existing AI tools already boost ad performance, with Advantage+ campaigns delivering $4.52 in revenue per $1 spent.   
        
    - **Conversational Commerce Growth:** The platform will accelerate Meta's push into conversational commerce, enabling seamless transactions directly within messaging apps. Features like multi-product message templates and in-chat payments on WhatsApp Business API will drive more commerce activity, from which Meta can take a share or generate additional ad revenue.   
        
2. **Deepening Business Engagement and Retention:**
    
    - **Indispensable Tool for Businesses:** By providing a leading AI agent platform, Meta makes its ecosystem even more critical for the 200 million businesses that already rely on it. This deep integration into core business operations (sales, support) fosters stickiness and reduces churn.
    - **Streamlined Operations for SMBs:** The platform can particularly benefit small-to-medium sized businesses (SMBs) who often lack the resources for extensive customer service or complex ad creation. Automating these functions makes Meta's platforms more accessible and valuable to a broader range of businesses.   
        
    - **Unified Customer Experience:** By spanning Meta's 1P channels (WhatsApp, Messenger, Instagram Direct) and 3P channels (webchat, SMS, phone), the platform offers businesses a unified solution for customer interactions, simplifying their tech stack and improving customer experience across touchpoints.
3. **Solidifying AI Leadership:**
    
    - **Applied AI Dominance:** This platform positions Meta as a leader in applied AI for business, complementing its efforts in consumer AI (Meta AI app, Ray-Ban AI glasses). It demonstrates Meta's ability to translate foundational AI research (like Llama 4) into tangible, high-value business solutions.   
        
    - **Data Flywheel:** More business interactions through AI agents generate more proprietary data on business-consumer conversations, sales patterns, and support queries. This data can then be fed back into Meta's AI models, continuously improving the agents' effectiveness and creating a powerful data flywheel.

## IV. Meta's Moat Against Other LLM Builders

While companies like OpenAI, Google, and Microsoft are building powerful foundational LLMs and offering API access, Meta has several distinct advantages that can form a strong moat for its Business AI Platform:

1. **Unparalleled First-Party Data and Social Graph:**
    
    - **Vast Interaction Data:** Meta's Family of Apps has nearly 4 billion monthly active people and facilitates over 600 million daily conversations across its messaging apps. This provides an immense, proprietary dataset of real-world human-to-human and human-to-business interactions, including conversations, interests, behaviors, and purchase signals. This data is invaluable for training and refining AI agents to understand nuanced human communication, intent, and context in a way generic LLMs cannot replicate.   
        
    - **Behavioral Insights:** Meta's AI-driven ad delivery and content recommendations are already highly effective due to deep user behavioral insights. This same understanding of user preferences and engagement can be directly applied to make business AI agents more effective at sales and support.   
        
2. **Native Integration with a Massive Business and User Ecosystem:**
    
    - **Existing Business Relationships:** Meta already serves 200 million businesses. This is a massive, pre-existing customer base that can be onboarded onto the AI agent platform, significantly reducing customer acquisition costs and leveraging established trust.
    - **Integrated Channels:** The Business AI Platform can seamlessly integrate across Meta's owned channels (WhatsApp, Messenger, Instagram Direct, Facebook). This offers businesses a unified, out-of-the-box solution for managing customer interactions across platforms where their customers already spend significant time. Other LLM providers typically offer APIs, requiring businesses to build and maintain these integrations themselves.
    - **Full-Funnel Commerce Integration:** Meta's core business is advertising and social commerce. The AI agents can be directly linked to ad campaigns (e.g., click-to-message ads ), turning ad impressions into direct sales conversations and support interactions. This full-funnel integration from discovery to conversion within a single ecosystem is a powerful differentiator that pure LLM providers lack.   
        
3. **Specialized Conversational Commerce Expertise:**
    
    - **Deep Domain Knowledge:** Meta has been investing heavily in business messaging and conversational commerce, recognizing that 80% of WhatsApp users message businesses weekly. They are building specific features like multi-product message templates and in-chat payments. This deep, practical understanding of how businesses and consumers interact in a conversational context for sales and support gives Meta an edge in building highly effective, purpose-built AI agents.   
        
    - **User Expectations:** Users on Meta's platforms already have certain expectations for messaging and interaction. Meta can leverage this familiarity to create intuitive and effective AI agent experiences.
4. **Massive, Dedicated AI Infrastructure:**
    
    - **Proprietary Models & Compute:** Meta is investing $64-72 billion in AI infrastructure by 2025, including developing its own Llama 4 models and Meta Training and Inference Accelerator (MTIA). This massive, dedicated compute power allows Meta to train and deploy highly specialized, custom-built LLMs specifically optimized for business interactions at scale, potentially outperforming general-purpose LLMs in this domain. This "compute land grab" is a significant long-term advantage.   
        
    - **Vertical Integration:** Unlike many LLM builders who rely on cloud providers, Meta's vertical integration of hardware, software, and AI models provides greater control over performance, cost, and innovation.
5. **Future-Proofing with AI Devices:**
    
    - While not immediate, Meta's long-term vision for AI devices like Ray-Ban Meta AI glasses could eventually extend the reach of these business AI agents into real-world, multimodal interactions. Imagine an AI agent assisting a retail associate through smart glasses or providing real-time product information to a customer in a physical store. This hardware-software synergy could create a unique, immersive moat that pure software LLM providers cannot easily replicate.   
        

In summary, Meta's moat against other LLM builders for its Business AI Platform lies not just in its underlying AI models, but in the **unparalleled combination of its vast, proprietary user data, deeply integrated ecosystem of popular apps, existing relationships with millions of businesses, specialized expertise in conversational commerce, and massive, dedicated AI infrastructure.** This allows Meta to build highly effective, natively integrated, and contextually rich AI agents that can drive real business outcomes and significant incremental revenue.

## V. Interview Preparation Focus Areas

For your interviews, particularly the Product Sense, Analytical Thinking, Execution, and Leadership rounds, you should be prepared to discuss these points with concrete examples from your experience.

- **Product Sense & Analytical Thinking:**
    - How would you identify the highest-impact business verticals and use cases for the AI platform?
    - How would you define success metrics for the AI agent's ability to "close sales" or be a "brand representative"?
    - How would you use data (e.g., conversation logs, conversion rates, customer feedback) to iterate and improve the AI agent's performance?
    - Analyze a hypothetical scenario where a business is struggling to adopt the AI agent – what's your diagnostic process?
- **Execution:**
    - How would you prioritize features for the core conversation experience and customization capabilities? (e.g., using frameworks like RICE, ICE, or a Meta-specific approach like "impact-driven goals" )   
        
    - How would you work with engineering and data science teams on cutting-edge GenAI development, especially given your experience with AI-based insights at Dell?   
        
    - Describe your process for launching a complex platform product that spans multiple internal and external channels.
    - How do you ensure team health and maintain velocity in a fast-moving, ambiguous environment?   
        
- **Leadership:**
    - How would you establish a shared vision for the Business AI Platform across diverse internal stakeholders (e.g., product teams for WhatsApp, Instagram, Ads, legal, sales)?   
        
    - How do you bring clarity and structure to ambiguous 0-1 opportunities, especially when building something entirely new like an AI agent platform?   
        
    - How would you influence and gain buy-in from businesses to adopt these new AI agents, particularly those concerned about ceding control to AI?   
        
    - How do you foster an "extreme growth mindset" within your team and across partner teams?

By deeply understanding these aspects of Meta's strategy and connecting them to your own impressive background, you will be well-positioned for success.