Staff Machine Learning Infrastructure Engineer, Embedding Platform

What you'll do

• Architect and lead the development of next-generation, large-scale machine learning techniques.
• Define and execute ML strategies to improve personalization and recommendation quality across the platform.
• Lead research on scalable machine learning systems and real-time model adaptation for production use.
• Build high-performance, distributed training systems that scale across multiple GPUs and cloud environments.
• Optimize real-time serving architectures for large-scale embeddings to ensure low-latency inference and high throughput.
• Integrate ML models into key AI-driven systems like Feed Ranking, Ads, and Content Understanding.
• Mentor and guide senior and mid-level engineers to foster a culture of technical excellence.
• Evaluate and introduce new modeling paradigms to keep the platform's machine learning ecosystem cutting-edge.

What we're looking for

• 8+ years of experience in machine learning engineering focusing on large-scale systems and recommendation or personalization systems.
• Expertise in modern deep learning architectures, including sequence models and foundational models.
• Deep understanding of modeling complex multi-entity relationships in large-scale machine learning applications.
• Proven ability to design, implement, and optimize scalable ML architectures from distributed training to real-time inference.
• Strong software engineering skills in Python, C++, or similar languages for high-performance computing and cloud-based pipelines.
• Demonstrated leadership in driving ML strategy, mentoring engineers, and influencing cross-functional teams.
• Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in production systems.
• Excellent communication skills to present complex ML concepts to both technical and non-technical stakeholders.