AI Engineer, Applied ML

About the Role

Perplexity is looking for an Applied ML Engineer to design, build, and iterate on cutting-edge AI models powering our core experience. As an expert in machine learning and artificial intelligence, you will develop scalable and impactful solutions for user personalization, query understanding, and content discovery - fulfilling the curiosity of millions of users across the globe.

Responsibilities

• Apply state-of-the-art ML and LLM techniques to solve problems spanning:
• Personalization (LLM memory, context summarization, retrieval and ranking);
• Query Understanding (intent modeling, rewriting, agentic decomposition);
• Content Discovery (feed ranking and surfacing)
• Rigorously evaluate LLM/ML models with both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact.
• Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement.
• Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements.
• Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle.

Requirements

• 5+ years experience building and shipping robust ML/AI models for large-scale, user-facing or data-driven products.
• Deep expertise in deep learning (PyTorch, TensorFlow, JAX), LLMs, information retrieval, content summarization, recommendation systems, NLP, and/or ranking.
• Strong software engineering skills (Python, production-quality codebases, collaborative development).
• In-depth experience with the full ML lifecycle: data analysis, feature engineering, iterative model development, rigorous evaluation, and ongoing monitoring/improvement.
• Proven collaborator and communicator; excels in high-velocity, cross-functional teams.
• Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI.
• BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience).

Qualifications

• Experience with LLM prompt engineering, Retrieval-augmented generation (RAG) based systems.
• Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).
• Open-source or published contributions in ML, NLP, IR, or relevant research fields.