Okay, I've searched for Research Engineer positions in Robotics and AI in Seattle, WA, requiring a Master's degree, 7+ years of experience, and skills in Python and PyTorch. I have found 34 relevant roles for you.

Sure. Here's the analysis:

Job Analysis:

This Research Engineer role at Meta FAIR is fundamentally about advancing the frontier of AI by developing sophisticated world models that enable machines to understand and predict physical environments, particularly through video inputs. The candidate is expected not only to contribute technically but also to push research innovations that blend machine learning, computer vision, reinforcement learning, and control systems within a robotics context. Success hinges on the ability to operate at the intersection of theory and scalable system design — designing experiments, managing large datasets, and building optimized tooling to accelerate research outcomes. Strong collaboration across diverse scientific and engineering teams is essential, given the cross-functional nature of FAIR’s projects. The role also demands a high level of autonomy in decision-making around research direction, experimental methods, and software infrastructure, while balancing the need for open dissemination of results and reproducibility. Candidates with a solid mix of software engineering skills (Python, PyTorch, optionally C++ and CUDA), hands-on research experience, and deep domain expertise in areas like self-supervised learning or model-based planning will thrive. Performance within the first year likely involves demonstrating new algorithmic innovations, contributing to top-tier publications, mentoring peers, and developing infrastructure that improves both the fidelity and efficiency of embodied AI models.

Company Analysis:

Meta, particularly through its Fundamental AI Research division, stands as a globally recognized leader in large-scale AI innovation and foundational research with a strong focus on long-term impact. This environment is highly dynamic, intellectually rigorous, and mission-driven, aiming to pioneer next-generation AI paradigms like world models for embodied agents. The culture favors deep technical excellence, continuous learning, collaboration, and openness — reflected in its emphasis on publishing research and releasing high-quality open-source code. Working at Meta FAIR means engaging with some of the brightest minds in the AI research community, implying a high-performance climate that can be simultaneously challenging and rewarding. The Seattle role connects to a larger cross-disciplinary ecosystem, providing visibility to both engineering leadership and scientific peers, thus amplifying influence across the research landscape. Meta’s strategic direction suggests this role is pivotal in progressing AI models that could redefine how intelligent agents perceive and interact with the world, making this not just an engineering position but a key contributor to Meta’s future AI vision and product applicability.

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

• Behavioral: Tell me about a time when you had to collaborate across multiple disciplines to achieve a complex research goal. How did you navigate differing priorities?
• Behavioral: Describe an instance where you encountered a significant obstacle in your research project. How did you approach solving it?
• Behavioral: How have you mentored junior engineers or researchers in your previous roles? What strategies did you find effective?
• Behavioral: Give an example of when you had to balance the demands of rigorous experimental research with software engineering constraints.
• Technical: Can you walk me through your approach to designing and optimizing a model-based reinforcement learning algorithm for robotic control?
• Technical: How do you handle the challenges involved in building large-scale datasets and evaluation benchmarks for video-based world models?
• Technical: Explain your experience with Python and PyTorch in implementing self-supervised learning architectures, especially joint-embedding predictive architectures (JEPAs).
• Technical: Describe your process for optimizing computational performance using C++ or CUDA in a research codebase.
• Situational: Imagine you notice a newly developed world model isn't generalizing well across different environments. How would you identify and address the root causes?
• Situational: Suppose you have limited labeled data but need to improve a robotic agent’s perception model. How would you leverage self-supervised learning in this context?
• Situational: If conflicting priorities arise between publishing experimental results quickly and ensuring the code is robust and reproducible, how would you balance these demands?
• Company Fit: Why are you interested in joining Meta's Fundamental AI Research team specifically, rather than other AI research organizations?
• Company Fit: How do you see your personal and professional values aligning with Meta’s commitment to open research and collaboration?
• Company Fit: Meta focuses on world modeling for embodied AI agents. How does this vision resonate with your career goals and research interests?
• Candidate to Ask: Could you share how research engineers at FAIR collaborate with product teams to transition world modeling innovations into real-world applications?
• Candidate to Ask: What are some current biggest technical or research challenges the team is facing in advancing model-based reinforcement learning?
• Candidate to Ask: How does FAIR measure the impact and success of research contributions beyond publications?