Staff Machine Learning Infrastructure Engineer, Embedding Platform

Reddit, Inc. is a community-driven platform built around shared interests, passion, and trust, serving millions of daily visitors through its communities and content. The Staff Machine Learning Infrastructure Engineer will lead the technical direction of large-scale ML platforms and embedding systems, define ML strategy, and advance scalable deep learning, distributed training, and real-time inference. The role also involves cross-functional collaboration, technical leadership, research, and mentoring of ML engineers.

Responsibilities:

• Architect and lead the development of next-generation, large-scale machine learning techniques
• Define and execute the ML strategy, identifying opportunities to enhance personalization and recommendation quality across Reddit
• Lead research initiatives on scalable machine learning systems and real-time model adaptation, bringing cutting-edge advancements into production
• Partner with ML infrastructure teams to build high-performance, distributed training systems that efficiently scale across multiple GPUs and cloud environments
• Establish and optimize real-time serving architectures for large-scale embeddings, ensuring low-latency inference and high throughput
• Collaborate cross-functionally with teams in Feed Ranking, Ads, Content Understanding, and Core ML to integrate ML models into Reddit’s key AI-driven systems
• Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing
• Stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit’s ML ecosystem cutting-edge
• Drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making

Requirements:

• 8+ years of experience in machine learning engineering, with a strong focus on large-scale ML systems and recommendation or personalization systems
• Expertise in modern deep learning architectures, including sequence models and foundational models
• Deep understanding of complex multi-entity relationships in machine learning applications and how they are modeled in large-scale systems
• 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, with experience in ML infrastructure, high-performance computing, and cloud-based ML 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 large-scale production systems
• Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders