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.

Since publicly launching the world’s first fully functional conversational answer engine a year ago, we’ve grown to 10 million monthly active users and have served over half a billion queries in 2023.To support our rapid consumer adoption and expansion plans, we’ve raised $73.6 million in Series B funding from trusted VC firms and prominent tech visionaries. IVP led the round with continued support from our Seed and Series A investors NEA, Elad Gil, Nat Friedman, and Databricks, as well as new investors NVIDIA, Jeff Bezos (through Bezos Expeditions Fund), Tobi Lutke, Bessemer Venture Partners, Naval Ravikant, Balaji Srinivasan, Guillermo Rauch, Austen Allred, Factorial Funds, and Kindred Ventures, among others. Building upon our Series A earlier this year, we’ve now raised $100 million to date.

Final offer amounts are determined by multiple factors, including, experience and expertise, and may vary from the amounts listed above.

Equity: In addition to the base salary, equity is part of the total compensation package.

Benefits: Comprehensive health, dental, and vision insurance for you and your dependents. Includes a 401(k) plan.

Machine Learning Engineer