Introduction

Perplexity is building AI systems that integrate directly into how enterprises operate. Our API Platform powers search, retrieval, and automation across structured and unstructured data, while Perplexity Computer extends this into the execution of an AI system that navigates tools, interacts with applications, and completes multi-step workflows. Together, these platforms form a new integration layer, connecting models, data, and enterprise systems into end-to-end workflows.

About the Role

We’re looking for Forward Deployed Engineers to work directly with customers to design and deploy these integrations in production. You’ll embed with teams, connect Perplexity into their existing stack, and build systems that automate real work.

This role spans two closely connected areas:

• API Platform: integrating search, retrieval, and AI capabilities into enterprise systems

• Perplexity Computer: deploying agentic workflows that operate across tools, applications, and data

Why Join Us

Perplexity’s platforms are redefining how AI is integrated into enterprise operations. You’ll work on cutting-edge AI systems, building end-to-end workflows and integrations that solve real business problems at scale.

This is a unique opportunity to influence both product and adoption, embed AI deeply in enterprise workflows, and shape the future of how organizations operationalize AI.

Responsibilities

• Design, build, and deploy end-to-end integrations between Perplexity and enterprise systems (data platforms, internal tools, SaaS applications), translating business workflows into production-grade AI systems

• Work directly with customer teams to embed AI into existing processes, owning deployments from initial architecture through production rollout and ongoing optimization

• Develop and operationalize integrations using APIs, event-driven architectures, and workflow orchestration, including deploying Perplexity Computer for multi-step, agent-driven workflows across tools and environments

• Design and build production systems that combine retrieval, reasoning, and execution across enterprise environments, applying deep expertise in LLM capabilities, implementation patterns, and the AI stack to drive performance, security, and customer impact

• Debug and resolve issues across APIs, infrastructure, and external dependencies, ensuring reliability, performance, and scalability in production

• Prototype new integration patterns and build reusable architectures that accelerate adoption across customers

• Partner with Sales and Product to unlock new use cases, drive expansion, and translate deployment learnings into product and platform improvements

What we're looking for

• 5+ years of experience in software engineering, forward deployed engineering, solutions engineering, or similar roles, with a track record of building and shipping production systems in customer-facing environments

• Strong programming ability in Python (plus one of JavaScript/TypeScript, Java, etc.) with experience developing integrations, prototypes, and scalable applications

• Deep experience with APIs and distributed systems, including authentication, latency optimization, and debugging across complex, multi-system environments

• Production experience building LLM-powered systems, including prompt engineering, agent workflows, evaluation, and deploying AI systems at scale

• Proven ability to design and implement automated, end-to-end workflows that integrate across enterprise systems and replace manual processes

• High ownership and ability to operate in ambiguous environments, with strong system design, rapid prototyping skills, and end-to-end execution

• Excellent communication and collaboration skills, with experience working cross-functionally and engaging both technical teams and executive stakeholders

Nice-to-Haves

• Experience with search systems, retrieval-augmented generation (RAG), or AI/ML APIs

• Background in developer tools, platform engineering, or high-scale/low-latency system design

• History of working at startups or small teams, owning customer projects end-to-end

• Experience with enterprise IT systems or AI deployment patterns in regulated industries (finance, healthcare, life sciences)