Principal Machine Learning Engineer - Large Scale Embedding - (Remote - US)

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Requirements:

• 15+ years of technical leadership experience in machine learning and AI.
• Proven ability to lead ML initiatives and communicate complex ideas effectively to cross-functional teams.
• Expertise in Graph Neural Networks, collaborative filtering, knowledge graphs, and recommendation systems.
• Strong coding proficiency in Python, with experience in ML frameworks like PyTorch Geometric, DGL, TensorFlow, and scikit-learn.
• Solid understanding of ML infrastructure components and libraries for efficient distributed training and inference.

Responsibilities:

• Lead the design and architecture of multi-entity embedding generation using GNN and transformers.
• Define the technical roadmap, working with cross-functional partners to align execution plans.
• Develop and optimize large-scale graph-based machine learning pipelines for recommendation systems.
• Ensure scalable and efficient architectures for processing complex, interconnected data.
• Collaborate with internal teams to improve relevance metrics and extend the use of models in upstream functions.
• Mentor and support the growth of your team while contributing to overall product strategy.