Principal Applied Scientist
Core
Design and run experiments, develop ML pipelines and models (encoder-decoder, cross-encoder, SLMs, model distillation) for focused ranking tasks with low latency constraints, and drive product innovation by translating vision into scalable architecture.
Role type
Principal Applied Scientist (Strategy & Mentorship)
Builds
Scalable, reliable ML architectures for ranking tasks and large-scale embedding-based models
Domain
Search, Ranking, Large Language Models (LLMs), Applied Science
Deliverable
production ML models
Required skills
[encoder-decoder models], [cross-encoder models], [SLMs], [model distillation], [large scale data analysis], [Privacy and Compliance], [product roadmap planning], [technical mentorship], [model evaluation best practices], [research program leadership]
Preferred skills
[publishing in academic/industry venues], [conference speaking], [cross-org influence]
Technologies
[encoder-decoder frameworks], [cross-encoder frameworks], [SLM frameworks], [model distillation tools], [large scale data platforms]
Responsibilities
[Design and run experiments to validate metrics], [Develop ML pipelines and models for ranking tasks], [Partner with Engineering/PM/Design to translate product vision], [Engage with customers to understand pain points], [Mentor team and scale learnings across product group], [Define best practices in Model evaluation]
Seniority
Principal, strategy & mentorship
