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PerplexityPerplexitySan Francisco, CA

Member of Technical Staff

Build and optimize prompts, tools, skills, and memory systems to shape how Perplexity's AI models respond, use tools, and leverage context across products. Requires strong software engineering skills and experience with LLM behavior design.

200k – 330k/yr
Hybrid2+ YOEML Engineering

About the role

What you'll do

  • 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.

Requirements

  • 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 shaping LLM behavior through prompts, tool and skill design, or memory systems.
  • Strong written and verbal communication skills, particularly in explaining complex concepts to diverse stakeholders.

Nice to have

  • Recent experience working on modern LLM-driven products.
  • Experience working across teams or with external partners.
  • Experience designing evaluations or benchmarks for AI systems.

Skills

LLMsPrompt Engineeringcontext engineeringPythonAgentic Systemsai evaluationsbenchmarks

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