Member of Technical Staff (Model Behavior)
Posted Updated
- We’re hiring software engineers for the Model Behavior team to help shape how Perplexity’s AI products behave: the style of their responses, and the way they use tools, skills, and memory
- The team designs prompt and context engineering strategies to deliver high-quality user experiences across multiple domains and models
- The ideal candidate for this role has a strong software engineering background, and an analytical, experiment-driven approach to solving challenging problems
- You’ll work on context and prompt engineering to shape model behavior and style, and to guide how models use tools, skills, and memory across our products
- Context Engineering: Design, test, and optimize the prompts, skills, tools, and memory that shape Perplexity responses across products, features, and use cases. Build self-improvement loops to steer the prompt, improve tool/skill use, and improve the ability to draw on memory
- Model Releases: Help experiment with and release new models
- Research & Analysis: Identify inconsistencies and failure modes in model outputs through well-designed research projects, for both internal and production systems
- Knowledge Sharing: Help engineers across teams build intuition for prompt design and context engineering best practices
- Staying Current: Track the latest prompting, context engineering, and alignment techniques from industry and academia, and bring the best ideas back to the team
- Strong written and verbal communication skills, particularly in explaining complex concepts to diverse stakeholders
- Experience shaping LLM behavior through prompts, tool and skill design, or memory systems
- 2 to 10+ years of experience in software engineering or research
- Strong background in software engineering fundamentals, and a technical understanding of LLM-driven and agentic systems
- Experience working across teams or with external partners
- Recent experience working on modern LLM-driven products
- Experience designing evaluations or benchmarks for AI systems