Staff Machine Learning Engineer, Embeddings Platform

How You'll Have Impact

As a Staff Machine Learning Engineer, you will own the technical direction for large-scale machine learning models, guiding the development of advanced deep learning architectures and high-impact ML systems. You will partner with leadership to define ML roadmaps, drive innovation in scalable model design and training approaches, and ensure efficient, reliable deployment of ML models in production. This role offers an opportunity to influence key AI-driven systems across Reddit while mentoring and uplifting the team’s technical capabilities.

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.