Member of Technical Staff (Software Engineer, Enterprise Agent Evals & Efficiency)

Job summary

Responsibilities

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.

• Build realistic enterprise evaluation environments spanning connected applications, representative datasets, user roles, and permissions, with complex, multi-step workflows and…
• Own evaluation infrastructure, task suites, and automated graders that measure how well agents complete enterprise work, calibrated against human judgment and grounded in…
• Analyze agent trajectories and production failures, identify root causes, and ship improvements across the agent harness, tools, and skills that measurably improve task success…
• 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…
• 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.

What they're looking for

• 5+ 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:

Tech stack

• Python
• Go
• Rust