Backed by Accel, Bessemer and NEA.
About the role
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. This team owns the improvement loop: define what successful work looks like, build evaluations that measure it, understand where agents fail, and ship changes that improve outcomes. Found on 1752vc Careers, the job board for startup and VC roles.
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
More about this role
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. This team owns the improvement loop: define what successful work looks like, build evaluations that measure it, understand where agents fail, and ship changes that improve outcomes.
This is a hands-on engineering role with end-to-end ownership, from building realistic enterprise evaluation environments to shipping improvements across the agent harness, tools, and skills. You'll create complex scenarios spanning connected systems, data, and permissions, use them to identify where agents struggle, and improve the tools agents call, the skills they follow, the context they receive, and the models they use. Working with AI Research, Connectors, and Platform teams, you'll turn experimental results into measurable gains in task success, latency, and cost per successful task. The goal is not just to measure agents, but to make more enterprise work reliable and practical to automate.
Build realistic...
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