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