Perplexity is building the next generation answer engine, empowering our users to find information in new and more effective ways. Perplexity applies LLMs for knowledge search at scale, serving millions of users across the world. In pursuit of this mission, we are looking for engineers to build recommendation systems for a more engaging, easier to use product. You will be part of a dynamic team dedicated to pushing the boundaries of what's possible with machine learning, particularly in the realms of content discovery and personalization.
Our current backend stack is Python, Rust, TensorRT-LLM, Kubernetes, AWS. You will work on designing and implementing machine learning models that drive our recommendation systems, retrieval algorithms, and classifiers.
Responsibilities
- Develop, train, and optimize machine learning models for recommendation systems.
- Build groundwork infrastructure for retrieval.
- Work with real world data to create scalable feedback loops.
- Incorporate state of the art LLMs into traditional machine learning systems.
Qualifications
- Experience with machine learning at scale.
- Experience with retrieval infrastructure.
- Creative problem-solving skills with an ability to explore a new landscape of models.
- Eagerness to build from 0 to 1 and unlock potential in new products.
- Familiarity with big data pipelines for feature engineering.
- At least 6 years of experience with machine learning and backend engineering.
The cash compensation for this role is $200,000 to $250,000.