Member of Technical Staff (Applied AI Engineer, Agent Capabilities)New$220K–$405K

Perplexity
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The opportunity

Perplexity Computer is one of the defining products of the new era of agentic AI. Millions of people use Perplexity to transform knowledge into action, and the Agent Capabilities team sits at the intersection of frontier AI research and product innovation, building the…

What you'll do

  • Evaluate frontier models against real user tasks, identify useful behaviors: and failure modes, and turn the most promising advances into production agent systems. Own the lifecycle from rapid prototyping and evaluation through launch, monitoring, and iteration.

  • Improve agents’ ability to plan, use tools, manage context, recover from: errors, and complete long-running tasks reliably.

  • Apply state of the art ML and LLM techniques to design scalable agent: capabilities such as skills, plugins, artifact generation, tools integrate and use, auto-research, and multi-agent collaboration. Shape the architecture, abstractions, and product experiences that enable both users and agents to compose increasingly sophisticated solutions for real-world tasks.

  • Own agent behavior and capabilities end-to-end, from user-facing products and: interfaces to backend services. Define offline and online evaluations for task completion, correctness, safety, latency, cost, and user satisfaction. Iteratively improve across models, prompts, harnesses, and products for different problem spaces.

  • Build secure, observable, and reliable agent systems, including permissions: and safeguards for sensitive actions. Develop tracing, replay, and monitoring infrastructure that makes agent failures reproducible and actionable.

  • Collaborate closely with PM, Data Science, Research, to identify high-impact: opportunities in understanding and validating emerging model capabilities, and turn complex agent behaviors into simple, reliable product experiences.

What they're looking for

  • Typically 6+ years of professional software engineering experience, with a: track record of building and owning robust AI-powered, large-scale, user-facing or data-intensive products. Exceptional candidates with less experience and an outstanding record of impact are encouraged to apply.
  • Strong software engineering fundamentals, with experience building and: operating AI/ML products, backend services, or distributed systems at scale.
  • Experience owning the AI product lifecycle, including data analysis, rigorous: evaluation, production monitoring, and iterative improvement. Able to define metrics and use production data and user feedback to guide decisions.
  • Practical experience in one or more relevant areas, such as agent harnesses,: tool use, context engineering, model evaluation, browser automation, or long-running task execution.
  • Strong product judgment and execution: you can translate ambiguous user needs into applied AI or ML problems and ship durable solutions with measurable user impact.
  • Genuine interest in frontier AI capabilities, agent systems, and excitement: for rapidly exploring, evaluating, and productizing new model behaviors.