Member of Technical Staff (Software Engineer, Enterprise Agent Evals & Efficiency)Active$220K–$405K

Perplexity
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The opportunity

The Enterprise Agent Evals & Efficiency team makes Perplexity's agents better at completing real work across enterprise connectors, tools, and data. Our goal is to help agents choose the right tools, use the right context, and complete complex workflows reliably, with less time and compute.

What you'll do

  • Build realistic enterprise evaluation environments spanning connected: applications, representative datasets, user roles, and permissions, with complex, multi-step workflows and reproducible system state.

  • Own evaluation infrastructure, task suites, and automated graders that: measure how well agents complete enterprise work, calibrated against human judgment and grounded in end-to-end outcomes.

  • Analyze agent trajectories and production failures, identify root causes, and: ship improvements across the agent harness, tools, and skills that measurably improve task success and reliability.

  • Develop reusable skills and improve tool descriptions, schemas, and result: handling so agents can select the right tools, execute workflows correctly, and recover from errors with fewer unnecessary calls.

  • Build and evaluate model-selection and routing strategies that match each: task to the right model and effort level, balancing quality, latency, reliability, and cost.

  • Improve agent efficiency through context management, skill loading, caching,: and execution strategies that reduce wasted tokens and cost per successful task without compromising quality or safety.

What they're looking for

  • + years of software engineering or machine learning engineering experience,: with a track record of owning complex production systems from design through deployment and operation.
  • Strong system design skills, including building reliable, scalable: infrastructure for experimentation, evaluation, or data-intensive applications.
  • Strong proficiency in a backend language such as Python, Go, or Rust, and the: ability to work effectively across a multi-language production stack.
  • Hands-on experience building, evaluating, or improving LLM-based applications: or agents, including tool use and multi-step workflows.
  • Deep expertise in at least one of the following: Evaluation infrastructure, benchmark design, and automated grading.
  • Agent harnesses, tool interfaces, and reusable skills for complex workflows.
  • Realistic test environments or simulations spanning multiple systems,: datasets, permissions, and stateful workflows.
  • Model selection, routing, and inference efficiency, including context management and caching.