Okay, I've searched for Research Engineer roles specializing in AI and machine learning in Sunnyvale, CA, full-time positions within the internet industry that require at least 2 years of experience and a Bachelor’s degree in Computer Science or related fields. I have found 154 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The role of Research Engineer, Monetization AI at Meta is fundamentally about advancing the frontier of AI and machine learning technologies to optimize Meta's monetization systems, primarily personalized advertising. This is a high-impact position that necessitates deep technical expertise in large-scale model development, especially in modern deep learning architectures such as large language models, sequence learning, and generative modeling. The emphasis on scalability, efficiency, and reliability highlights the complexity of deploying models at Meta’s massive scale, where small improvements can translate into billions in revenue. The candidate will be expected to harness cutting-edge techniques including reinforcement learning for long-term value optimization, causal inference to understand data relationships, and AutoML for streamlined model development. Working cross-functionally implies close collaboration with hardware teams and other ML specialists, indicating the need for strong communication and teamwork skills alongside technical mastery. Success in this role looks like driving both foundational scientific research and practical system improvements that enable Meta to maintain its leadership in ad personalization. The role demands autonomy, creative problem solving to navigate tradeoffs between model accuracy and computational/resource constraints, and the ability to translate research insights into deployable products. Candidates with advanced degrees and a record of peer-reviewed publications are preferred, reflecting a culture valuing evidence-based innovation and rigor.

Company Analysis:

Meta occupies a dominant position in the internet and social media ecosystem, recognized not only for its vast user base but for its cutting-edge use of AI to drive business objectives such as personalized advertising. The Monetization AI team operates within a fast-paced, innovation-driven culture that values scientific excellence and impact at scale. While the job is posted via Lensa, the actual employer is Meta, a leader known for pushing the boundaries of technology. Meta’s work environment tends to be collaborative yet high-stakes, requiring employees who thrive on solving complex problems with broad organizational impact. Given Meta’s commitment to diversity and inclusion, candidates will find a culture that embraces varied perspectives but also expects rigorous performance. For the Research Engineer role, the organizational context is that of a critical node bridging research innovation and production systems, often interfacing directly with product and infrastructure teams. This hire is strategic — intended not simply to maintain but to accelerate Meta’s leadership in AI-driven monetization. The role offers significant visibility due to its direct influence on revenue-generating products, with opportunities for professional growth shaped by the company's ambition to stay at the forefront of AI research and deployment.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time you had to develop a machine learning model under resource or scalability constraints. How did you approach the challenge?
• Behavioral: Describe an experience where you collaborated across functions (e.g., researchers, engineers, or hardware teams) to solve a technical problem. What was your role and outcome?
• Behavioral: Share an example of when you transitioned an AI research idea from concept to production. What were the key challenges and how did you overcome them?
• Behavioral: Can you talk about a time when you had to weigh tradeoffs between model complexity and inference efficiency? How did you make your decision?
• Technical: How would you design a scalable training pipeline for large language models aimed at personalized ad ranking?
• Technical: Explain how you would apply reinforcement learning techniques, such as RLHF or long-term value optimization, to improve ad monetization systems.
• Technical: Discuss your experience with AutoML pipelines. What considerations are crucial when deploying such pipelines at scale in production?
• Technical: What approaches would you use to debias or adapt models for cold-start scenarios in recommendation or ranking systems?
• Situational: Imagine that a deployed ranking model starts showing degraded performance on key metrics after a data distribution shift. How would you investigate and respond to this issue?
• Situational: You are working on a generative model for data augmentation and notice it introduces bias into downstream models. What steps would you take to detect and mitigate this bias?
• Situational: Suppose you need to convince stakeholders to adopt a novel AI technique that has good research results but is unproven in production. How would you approach this situation?
• Company Fit: Why does working on AI-driven monetization at Meta excite you, and how do you see this aligning with your career goals?
• Company Fit: How do you envision contributing to a cross-functional team within Meta’s Monetization organization?
• Company Fit: Meta values continuous innovation and rigorous research. Can you describe how your approach to problem-solving fits within this culture?
• Questions for Interviewer: How does the Monetization AI team prioritize between exploratory research and production-focused engineering?
• Questions for Interviewer: Can you describe the collaboration dynamic between research engineers and hardware/software infrastructure teams?
• Questions for Interviewer: What are the biggest challenges the Monetization AI team expects to tackle over the next year?