machine learning engineer in search technologies

Perplexity develops advanced search technologies focused on retrieval and ranking, helping users search for and discover information.

• Contribute to experiments that improve search quality through better models, data usage, and evaluation tools under the guidance of senior engineers;
• Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models;
• Train and evaluate models, including LLM-based approaches, for retrieval, ranking, and classification tasks;
• Support the deployment and monitoring of search and ranking models in a scalable and performant way;
• Help build and iterate on RAG pipelines for grounding and answer generation;
• Collaborate with Data, AI, Infrastructure, and Product teams to deliver improvements quickly and learn best practices in production ML.

• Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems;
• Experience with Python and common ML frameworks such as PyTorch, TensorFlow, or JAX through academic, open-source, or personal projects;
• Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment;
• Nice to have: experience evaluating model quality using offline metrics and/or A/B testing, previous experience in internships, research, or significant projects involving search, recommendation, or NLP, experience with Rust.

• 12–24-Week internship;
• Volunteer position.