Staff Machine Learning Systems Engineer, Embeddings Platform

What You’ll Do

• 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.

Who You Might Be

• 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.