# Member of Technical Staff (Software Engineer, Agentic Enterprise)
**Company:** [Perplexity AI](https://scaleengineer.com/companies/perplexity)
Join Perplexity AI's Enterprise Adoption team as a Member of Technical Staff to architect AI agent systems that transform how organizations work. This role requires 4+ years of software engineering experience with deep expertise in modern AI agent frameworks (agents, connectors, context engineering, evals), Python proficiency, and strong product judgment to deliver enterprise-grade agentic solutions that integrate seamlessly with business operations.
**Role:** Backend Engineer
**Seniority:** Senior
**Locations:** San Francisco
**Salary:** 220000–405000 USD
[Apply](https://jobs.ashbyhq.com/perplexity/2c5fdd71-472a-4c62-bc53-deafb66e7941)
Canonical: https://scaleengineer.com/jobs/perplexity/member-of-technical-staff-software-engineer-agentic-enterprise
---
## Responsibilities

- Architect and Build Core Agent Systems: Design and implement foundational systems that enable Perplexity Computer to function as a transformative multiplier for enterprise and internal teams. Build the infrastructure that connects AI agents to business processes across recruiting, finance, legal, operations, support, and go-to-market functions, ensuring reliability and scalability at the enterprise level.
- Engineer Agent Connectors and Skills Infrastructure: Develop connectors, custom skills, and evaluation infrastructure that make knowledge stores, business systems, and human-orchestrated processes legible to AI agents. Implement MCP servers, context engineering frameworks, and integration patterns that allow agents to operate effectively across diverse business domains and technical architectures.
- Drive Product-Led Innovation from Internal Adoption: Partner with internal teams to identify highest-potential opportunities for agentic automation, translating friction points and successes into core product and platform improvements. Close feedback loops between real-world agent usage and product development, ensuring Computer evolves based on authentic enterprise working patterns.
- Collaborate Across Disciplines on Enterprise Solutions: Work cross-functionally with Product Managers, Design, Data Science, Sales Engineers, and enterprise customers to turn process requirements into simple, reliable product experiences. Build deep empathy for non-technical stakeholders' workflows and translate their needs into technically sound agent-driven solutions.
- Evaluate and Optimize Agent Performance: Design and implement evaluation frameworks that assess agent capabilities, reliability, and business impact. Monitor agent performance metrics, identify failure modes, and iterate on prompts, connectors, and skill implementations to continuously improve user experience and business outcomes.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Formal education in Computer Science, Software Engineering, Mathematics, or related discipline providing foundational knowledge of algorithms, data structures, system design, and computer architecture."}

### technical

- {"name":"Python Proficiency","description":"Advanced proficiency in Python for building agent systems, data processing, and backend services. Experience with Python frameworks and libraries commonly used in AI/ML applications and enterprise software development."}
- {"name":"AI Agent Framework Knowledge","description":"Deep understanding of modern AI agent architectures and frameworks including agent orchestration patterns, agentic reasoning loops, LLM integration, and evaluation methodologies. Familiarity with agent concepts such as tool use, planning, memory management, and multi-step task execution."}
- {"name":"MCP and Integration Protocols","description":"Working knowledge of Model Context Protocol (MCP) and similar standards for connecting AI models to external systems. Understanding of when, where, and why to use MCP servers for extending agent capabilities and integrating with enterprise software."}
- {"name":"Context Engineering and Prompt Design","description":"Ability to architect effective context engineering strategies that improve agent reasoning and reliability. Experience designing prompts, system instructions, and information architecture that enhance agent decision-making in complex business scenarios."}
- {"name":"TypeScript, Go, and Multi-Language Development","description":"Experience with TypeScript and Go for building scalable backend services, APIs, and integrations. Comfortable working across multiple programming languages and selecting the right tool for specific system requirements."}
- {"name":"Backend and API Development","description":"Strong experience designing and building scalable backend systems, RESTful APIs, and microservices architectures. Understanding of database design, system design principles, and performance optimization for enterprise applications."}
- {"name":"LLM Integration and Prompt Engineering","description":"Experience integrating frontier large language models into production systems. Deep familiarity with latest LLM capabilities, limitations, and techniques for reliable prompt engineering and output processing in critical business workflows."}

### experience

- {"name":"4+ Years Professional Software Engineering","description":"Minimum four years of professional software engineering experience demonstrating proficiency in building production systems, working with complex codebases, and collaborating effectively within engineering teams. Experience should include building scalable backend systems and working with enterprise or internal platform challenges."}
- {"name":"Enterprise or Internal Platform Development","description":"Experience building platforms, tools, or systems for enterprise customers or internal organizational use. Understanding of enterprise requirements, security considerations, reliability expectations, and the challenges of supporting diverse user technical abilities and business workflows."}
- {"name":"Daily AI Tool and Model Usage","description":"Regular, hands-on experience with frontier AI models and tools in professional and personal contexts. Deep familiarity with current state-of-the-art LLM capabilities, limitations, and rapidly evolving agent technologies that informs product decisions."}
- {"name":"Cross-Functional Stakeholder Collaboration","description":"Experience collaborating with non-technical stakeholders including business operations, product management, sales, and other business functions. Ability to understand diverse organizational needs and translate business requirements into technical solutions."}

## Skills

### required

- {"name":"Python","description":"Production-grade Python development for building backend services, AI integrations, and enterprise systems"}
- {"name":"AI Agent Architecture","description":"Design and implementation of multi-step reasoning agents, tool orchestration, and agentic workflows"}
- {"name":"Backend Systems Design","description":"Building scalable, reliable backend systems and APIs that support enterprise workloads"}
- {"name":"LLM Integration","description":"Integrating frontier language models into production systems with proper error handling and reliability patterns"}
- {"name":"Context Engineering","description":"Architecting effective context and information structures that improve agent reasoning and decision-making"}
- {"name":"Product Thinking and User Empathy","description":"Understanding end-user needs, designing simple experiences despite technical complexity, and making decisions that prioritize user success"}
- {"name":"System Integration and Connectors","description":"Building reliable connectors and integrations between AI agents and external business systems"}

### preferred

- {"name":"TypeScript","description":"Experience building backend services and APIs with TypeScript, particularly for AI/agent applications"}
- {"name":"Go","description":"Experience with Go for building high-performance backend services and microservices"}
- {"name":"Model Context Protocol (MCP)","description":"Familiarity with MCP specification and implementation for extending AI agent capabilities"}
- {"name":"Agent Evaluation Frameworks","description":"Experience designing evals, metrics, and testing frameworks for agent systems"}
- {"name":"Business Process Automation","description":"Prior experience automating business processes or building tools for internal operations teams"}
- {"name":"Enterprise Software Architecture","description":"Familiarity with enterprise security, compliance, data governance, and scalability requirements"}
- {"name":"Vector Databases and Embeddings","description":"Experience with embeddings, vector search, and retrieval-augmented generation (RAG) for agent context"}
- {"name":"Prompt Engineering and Tuning","description":"Advanced techniques for prompt optimization, few-shot learning, and reliable output formatting"}

## Tech stack

### tools

- {"name":"Git / GitHub","description":"Version control and collaborative development within engineering teams"}
- {"name":"Docker","description":"Containerization for deploying agent systems and backend services"}
- {"name":"Kubernetes","description":"Container orchestration for scalable agent infrastructure"}
- {"name":"Model Context Protocol (MCP)","description":"Standard protocol for connecting AI models to external tools and data sources"}
- {"name":"OpenAI API, Anthropic API","description":"Integrating frontier LLMs into agent systems and applications"}

### others

- {"name":"Agentic AI Architecture","description":"Multi-step reasoning, tool use, planning, and dynamic decision-making in AI systems"}
- {"name":"Retrieval-Augmented Generation (RAG)","description":"Combining vector search and LLMs to provide agents with grounded, contextual information"}
- {"name":"Prompt Engineering and Optimization","description":"Techniques for designing reliable, effective prompts and system instructions for agents"}
- {"name":"Agent Evaluation and Evals","description":"Building frameworks to measure agent performance, reliability, and business impact"}
- {"name":"Enterprise Integration Patterns","description":"Connecting agent systems to legacy enterprise software, APIs, and business processes"}

### databases

- {"name":"Vector Databases (Pinecone, Weaviate, Qdrant)","description":"Storing and retrieving embeddings for RAG, semantic search, and agent context management"}
- {"name":"PostgreSQL","description":"Relational database for structured business data and enterprise applications"}
- {"name":"Redis","description":"In-memory cache for high-performance agent state management and request handling"}

### languages

- {"name":"Python","description":"Primary language for AI agent systems, backend services, and integrations"}
- {"name":"TypeScript","description":"For building modern, type-safe backend services and APIs"}
- {"name":"Go","description":"For high-performance microservices and system components"}

### frameworks

- {"name":"FastAPI","description":"Modern Python framework for building fast, production-grade APIs and backend services"}
- {"name":"LangChain","description":"Framework for building applications with LLMs, agents, and composable components"}
- {"name":"LlamaIndex","description":"Data indexing and retrieval framework for building context-aware agent systems"}
- {"name":"Pydantic","description":"Python data validation and serialization using Python type annotations"}

## Benefits

### benefits

- {"name":"Work on Frontier AI Technology","description":"Build systems at the cutting edge of AI agent technology, working with the latest large language models and agentic frameworks that are reshaping enterprise work"}
- {"name":"High-Impact Internal Adoption","description":"See your work immediately impact Perplexity's internal operations and engineering velocity across recruiting, finance, legal, operations, and go-to-market teams"}
- {"name":"Enterprise Customer Influence","description":"Translate internal learnings into product improvements that directly benefit Perplexity's growing enterprise customer base"}
- {"name":"Cross-Functional Collaboration","description":"Partner with product managers, designers, data scientists, and business leaders to solve complex organizational challenges"}
- {"name":"Rapid Learning Environment","description":"Work in an AI-native company culture where continuous learning about frontier models and agentic patterns is core to daily work"}
- {"name":"Equity Compensation","description":"Participate in company growth through competitive equity packages alongside base compensation"}

## Compensation

- **max:** 280000
- **min:** 180000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Initial Screening Call","description":"Conversation with recruiter to understand your background, experience with AI tools, and familiarity with agentic AI concepts. Typical duration: 30 minutes."}
- {"name":"Technical Phone Screen","description":"Deep technical discussion with a member of the engineering team covering your experience building backend systems, LLM integrations, and your understanding of modern AI agent architectures. Expected topics: system design principles, agent frameworks, production backend experience."}
- {"name":"Take-Home Technical Assignment","description":"Design and coding exercise focused on building an AI agent integration or connector. May involve designing context engineering strategies, integrating with an LLM, or architecting a multi-system integration. Typically takes 2-4 hours."}
- {"name":"Technical Deep Dive Interview","description":"On-site or video interview reviewing your take-home assignment and discussing your approach to system design, agent architecture decisions, and trade-offs. Interview with 1-2 senior engineers from the Enterprise Adoption team."}
- {"name":"Product and Cross-Functional Discussion","description":"Conversation with product manager and/or business stakeholder to assess your product judgment, ability to understand non-technical user needs, and empathy for how different business functions work. This evaluates your ability to collaborate cross-functionally."}
- {"name":"Leadership and Values Alignment","description":"Final round with a member of leadership to discuss your career goals, learning interests, collaboration style, and alignment with Perplexity's mission in building the agentic future of enterprise work."}

## Full description
Perplexity is seeking creative, AI native engineers to join our Enterprise Adoption team. Our company is reshaping the way people interact with AI agents within organizations. The Enterprise Adoption team's mandate is to continually uplevel the way our company and our customers use Perplexity Computer as the instrument of that transformation to deliver new ways of working in the agentic future.

Just as Perplexity is heralding an agentic future for the internet, you will ensure that the company makes our AI tools and agents a perfect fit for our growing Enterprise business. Today, Perplexity uses AI tools across everything we do, from frontend and backend engineering to applied AI research and business operations. Some of your users will be our own teams, from recruiting and go-to-market to finance, legal, support, and operations, and you will engineer the core systems that make Computer an indispensable multiplier of their craft. Learnings from our internal teams will also be applied for our enterprise customers, bringing frontier AI working patterns to external organizations.

## What you’ll do

* Identify, prioritize, and execute on the highest-potential opportunities to make Computer transformative for every team at Perplexity, building the core systems that bring those opportunities within reach.
* Develop empathy for the nuances of our technical and business teams' work across disciplines and verticals (recruiting, sales, finance, support, legal, and operations), toward collaborating with them to harness AI in new ways.
* Make our knowledge stores, systems, and human-orchestrated processes legible to Computer, engineering the connectors, skills, and evaluation infrastructure that unlock each team's use cases at scale.
* Close the loop between internal usage and the Computer product itself, translating the friction and triumphs of real work into core product and platform improvements.
* Collaborate closely with PM, Design, Data Science, Sales, and Enterprise customers to turn any process need into simple, reliable product experiences in Computer.

## Qualifications

* 4+ years of professional software engineering experience. Familiarity with building platforms for enterprise or internal users is a plus.
* Daily use of AI models/tools, with strong knowledge on the latest frontier.
* Proficiency with Python, along with experience and/or TypeScript, Go, and other languages.
* Strong understanding of the work of non-technical staff across enterprises, along with a curiosity about the work of business and operations teams, who will be your users.
* Broad working knowledge of the modern AI agent stack (agents, connectors, context engineering, and evals), along with strong intuition for where today's agents shine and where they fall down. (For example: you don't need to have authored an MCP server, but you should know what MCP is and when/where/why it's used.)
* Strong product judgement, you can make decisions that will simplify the agent user experience for customers with a range of technical abilities.