Member of Technical Staff (AI Software Engineer, Agent Engine)

Perplexity•San Francisco, CA
•$250,000 - $405,000

About The Position

Perplexity is seeking an energetic AI Software Engineer to join its Agent Engine team. The team designs and builds agentic experiences, including Perplexity Computer, the Comet ecosystem, and the Agent API. The role combines AI expertise, product intuition, and a hands-on engineering approach to advance agent capabilities for Perplexity users. The engineer will work across engineering and applied research on open AI problems, including building frontier agent harnesses for high-stakes work, developing action and decision models for complex multimodal states, creating systems that work across local and cloud execution environments, implementing secure-by-design agent capabilities, and designing effective data representations and agent-environment interactions.

Requirements

  • Strong foundational familiarity with the AI systems stack.
  • Proficiency in Python.
  • Significant experience in at least one of the following areas: harness engineering; agent skills and plugin architectures; model training and reinforcement learning; or browser technologies such as CDP, Playwright, and extension development.
  • Strong technical intuition and a taste for user experience excellence.
  • Comfort working with a small, fast-moving team and a bias toward ownership.
  • Passion for shipping products that surprise and delight.

Nice To Haves

  • Experience with TypeScript, Go, and/or Rust.

Responsibilities

  • Design frontier agent harnesses and systems for high-stakes, mission-critical work.
  • Leverage action and decision models to accomplish user-specified objectives based on complex multimodal states.
  • Design systems and flexible abstractions that provide excellent experiences across local execution, headless cloud, custom executors, and other environments.
  • Develop permission architectures, payload classifiers, and other methods for secure-by-design agent capabilities.
  • Design data representations and interaction modes between agents and their environments.
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