Applied AI Engineer to design, build, and iterate on cutting-edge agents powering our core experience in Perplexity Computer. Working in this mission critical team, you will develop frontier context layer applications - fulfilling the curiosity of millions of users across the globe.
Key Responsibilities- Apply state-of-the-art ML and LLM techniques to solve problems spanning:
- Personalization (LLM memory, context summarization, retrieval and ranking);
- Contextual recommendations and Monetization applications
- Build frontier agent capabilities on top of Perplexity Computer
- Build auto research harness for 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 and build autonomous harness for agent squad to explore different problem spaces.
- 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.
Preferred Qualifications- 5+ years experience building and shipping robust AI products for large-scale, user-facing or data-driven products.
- Strong software engineering skills (Python, production-quality codebases, collaborative development) and experience using agentic coding tools for large scale parallel developments.
- In-depth experience with the full AI lifecycle: data analysis, 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).
Bonus Points For- Experience with LLM context engineering or harness engineering.
- Experience in mid-training or post-training frontier open source models
- Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).
