Senior Machine Learning Engineer, LS Embedding

The LS Embedding team focuses on developing highly expressive, multi-entity embeddings to enhance Reddit’s recommendation systems. We go beyond standard two-tower architectures, leveraging graph-based modeling, Graph Neural Networks (GNNs), and transformer-based architectures to capture complex interactions between users and entities. Our work directly impacts personalization and relevance across Reddit’s platform.

We are seeking a Senior Machine Learning Engineer to design, develop, and optimize graph-based ML models for large-scale recommendation systems. You will work on embedding generation, distributed training, and scalable serving architectures, playing a key role in improving Reddit’s AI-powered personalization. This role offers the opportunity to contribute to cutting-edge ML research and apply it at scale in a high-impact production environment.

• Design and implement scalable, high-performance machine learning models using Graph Neural Networks (GNNs), transformers, and knowledge graph approaches.
• Develop and optimize large-scale embedding generation pipelines for Reddit’s recommendation systems.
• Collaborate with ML infrastructure teams to enable efficient distributed training (multi-GPU, model/data parallelism) and low-latency serving.
• Work closely with cross-functional teams (Ads, Feed Ranking, Content Understanding) to integrate embeddings into various personalization and ranking systems.
• Drive feature engineering efforts, identifying and curating expressive raw data to enhance model effectiveness.
• Monitor, evaluate, and improve model performance using A/B testing, offline metrics, and real-time feedback loops.
• Stay up-to-date with the latest research in GNNs, transformers, and representation learning, bringing new ideas into production.
• Participate in code reviews, mentor junior engineers, and contribute to technical decision-making.

• 5+ years of experience in machine learning engineering, with a strong focus on recommendation systems, representation learning, and deep learning.
• Hands-on experience with Graph Neural Networks (GNNs), collaborative filtering, and large-scale embeddings.
• Proficiency in Python and experience with ML frameworks such as PyTorch Geometric (PyG), Deep Graph Library (DGL), TensorFlow, or JAX.
• Strong understanding of graph theory, network science, and representation learning techniques.
• Experience building distributed training and inference systems using ML infrastructure components (data parallelism, model pruning, inference optimization, etc.).
• Ability to work in a fast-paced environment, balancing innovation with high-quality production deployment.
• Strong communication skills and the ability to collaborate cross-functionally with engineers, researchers, and product teams.

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k Match
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Reddit Global Days off
• Generous paid Parental Leave  
• Paid Volunteer time off