Principal Machine Learning Engineer - Large Scale Embedding

The LS Embedding team will focus on building highly expressive multi-entity large scale embeddings exploring architectures beyond standard two-tower approaches to enhance our recommendation systems at Reddit. This entails modelling various compound interactions and relationships between users and entities they interact with using Graphs and exploring Graph neural networks and transformers to encode them.

We are looking for a Principal Machine Learning Engineer to lead the design and architecture of GNN and transformers based multi-entity embedding generation actively participating in end-to-end implementation process including enabling efficient distributed training and serving for such architectures shaping the future of recommendation systems at Reddit.

If applying ML / AI in production to improve Reddit Relevance excites you, then you’ve found the right place.

RESPONSIBILITIES:

• Lead the team that architects and designs GNN and transformers based multi-entity embedding generation.
• Define the technical roadmap and plan of execution in collaboration with Xfn partners.
• Develop and optimize large-scale graph-based machine learning pipelines for recommendation systems.
• Architect scalable and efficient GNN and transformers-based recommendation models that can process complex, interconnected data structures.
• Collaborate with cross functional business units such as Ads teams leveraging the models for upstream functions and improve relevance metrics.
• Collaborate with ML Infrastructure teams to enable distributed GPU based training and online serving architecture
• Lead feature engineering efforts to identify and curate expressive raw data to be used for creating embeddings
• Be a mentor and cross-functional advocate for the team
• Contribute towards team and product strategy, operations and execution at Reddit.

QUALIFICATIONS:

• 15+ years of Technical Leadership Experience
• Proven ability to lead ML initiatives, mentor engineers, and communicate complex concepts to cross-functional teams.
• Expertise in Graph Neural Networks, collaborative filtering, knowledge graphs, and deep learning for recommendations.
• Understanding of graph theory, network science, and representation learning technique
• Strong coding skills in Python and experience with ML frameworks like PyTorch Geometric (PyG), Deep Graph Library (DGL), TensorFlow, and scikit-learn.
• Solid understanding of ML infrastructure components and libraries (data parallel, model parallel, pipeline parallel, torch.inductor, model pruning, etc.) enabling efficient distributed training and inference.

Benefits:

• 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