Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit
www.redditinc.com.
The LS Embedding Machine Learning team is at the forefront of building highly expressive machine learning models that power Reddit’s recommendation systems. We go beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit’s ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale.
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