Member of Technical Staff (Software Engineer, Enterprise Adoption)

Perplexity AIYesterday

Location

San Francisco

Type

Full Time

Salary

USD 220,000 – 405,000

Level

Senior

Role

Backend Engineer

Posted

Jul 24, 2026

Full TimeSenior

The role

Summary

Join Perplexity AI's Enterprise Adoption team as a Member of Technical Staff to architect AI-powered systems that transform how enterprises leverage Computer agents. In this 4+ years experience software engineering role, you'll design the connectors, knowledge systems, and infrastructure that enable internal and external teams to harness frontier AI at scale, working across recruiting, sales, finance, operations, and customer success to translate real-world workflows into cutting-edge product experiences.

What you'll do

Identify and Execute High-Impact Opportunities: Analyze opportunities to make Perplexity Computer transformative across internal teams, building core systems and infrastructure that unlock new AI-native workflows for recruiting, sales, finance, legal, support, and operations functions.

Develop Cross-Functional AI Expertise: Build deep empathy and understanding of non-technical and technical teams' workflows across multiple business disciplines, collaborating with stakeholders to design and engineer AI solutions that address specific business use cases and operational challenges.

Engineer Knowledge Systems and Connectors: Design and implement the technical connectors, context engineering systems, evaluation infrastructure, and skills frameworks that make enterprise knowledge stores and business processes legible to AI agents, enabling seamless integration at scale.

Drive Product Feedback Loop: Bridge internal product adoption and external customer needs by translating real-world workflow friction and successes into core platform improvements, acting as a technical advocate for user needs within product and engineering teams.

Collaborate on Enterprise Solutions: Partner with Product, Design, Data Science, Sales, and enterprise customers to translate complex process requirements into simple, reliable, and intuitive product experiences within the Computer platform, ensuring both internal and external user adoption.

What we look for

Technical

Python ProficiencyStrong proficiency in Python for building robust backend systems, AI integrations, and data processing pipelines. Python is the primary language for AI development and agent integration work at Perplexity.

Modern Programming LanguagesWorking experience with TypeScript, Go, and other contemporary languages. TypeScript fluency for full-stack agent platform development and Go for high-performance backend services is particularly relevant to this role.

AI Agent Stack KnowledgeComprehensive understanding of the modern AI agent architecture including agents, connectors, context engineering (RAG, prompt engineering), and evaluation frameworks. Familiarity with protocols like Model Context Protocol (MCP) and when to leverage them for integration patterns.

Systems Design and ArchitectureExperience designing scalable backend systems with strong fundamentals in API design, data architecture, and system integration patterns. Ability to architect solutions that bridge AI capabilities with existing enterprise systems.

Database and Data SystemsProficiency with modern database technologies, data pipelines, and storage systems required for managing knowledge bases, context windows, and evaluation data for AI agents at enterprise scale.

Education

Computer Science or Related FieldBachelor's degree in Computer Science, Engineering, Mathematics, or equivalent professional software engineering experience. Strong theoretical foundation in computer science principles is valued but professional experience may substitute.

Experience

Enterprise or Platform Software Engineering4+ years of professional software engineering experience with demonstrated expertise building scalable backend systems. Prior experience developing platforms for enterprise users or internal adoption is highly valued and demonstrates understanding of enterprise adoption challenges.

Frontier AI Model ExperienceActive, daily hands-on experience with state-of-the-art AI models and tools, including current frontier models. Deep familiarity with the latest AI capabilities, limitations, and emerging paradigms in the AI agent ecosystem.

Cross-Functional Business UnderstandingDemonstrated ability to understand and empathize with non-technical staff workflows across enterprise functions including recruiting, sales, finance, operations, legal, and support. Curiosity about how business and operations teams work is essential for designing user-centric solutions.

Skills

Required skills

PythonCore backend language for building AI integrations, agent systems, and data pipelines within Perplexity's ecosystem.

AI Agent ArchitectureDeep working knowledge of agent-oriented systems including agentic reasoning loops, tool use, function calling, and multi-agent coordination patterns.

Backend Systems DesignStrong ability to architect scalable, reliable backend systems with focus on API design, service-oriented architecture, and system integration patterns.

Context Engineering (RAG)Practical expertise in retrieval-augmented generation, prompt engineering, and knowledge base construction to enhance AI agent capabilities and reliability.

Evaluation and ObservabilityExperience building evaluation frameworks, monitoring systems, and observability infrastructure for assessing and improving AI agent performance in production environments.

Product ThinkingStrong product judgement and ability to make user-centric architectural decisions that simplify experiences for users with varying technical abilities, balancing technical elegance with usability.

Nice to have

TypeScriptExperience with TypeScript for building full-stack agent applications, API development, and frontend-backend integration work.

GoFamiliarity with Go for building high-performance backend services, microservices, and systems-level applications that support agent infrastructure.

Model Context Protocol (MCP)Understanding of MCP as a standard for building connectors and integrations between AI models and external systems, including when and how to implement MCP servers.

Enterprise SaaS ExperiencePrior experience building or scaling SaaS products for enterprise customers, understanding enterprise adoption challenges, security requirements, and customer success considerations.

Internal Developer PlatformsExperience designing and building platforms for internal users, understanding how to create tools that drive adoption across non-technical teams and business functions.

Vector Databases and EmbeddingsHands-on experience with vector databases like Pinecone, Weaviate, or similar, and understanding embeddings for semantic search and retrieval optimization.

LLM FrameworksPractical experience with frameworks like LangChain, LlamaIndex, or similar tools for building composable AI applications and managing complex agent workflows.

Business Process AutomationExperience automating and optimizing business workflows, particularly in areas like recruiting, sales operations, finance, or customer support.

Compensation & benefits

Salary

USD 220,000 – 405,000 (annual)

Stock options

Available

Benefits

Equity Compensation

Competitive stock option grants as a member of technical staff, providing upside participation in Perplexity AI's growth trajectory as a leading AI answer engine company.

Comprehensive Health Coverage

Full medical, dental, and vision insurance plans, reflecting competitive Bay Area compensation standards for senior engineering talent.

Professional Development

Access to learning resources, conference attendance, and opportunities to stay current with rapidly evolving AI research, agent architectures, and frontier models.

Remote Work Flexibility

Ability to work with modern distributed teams building cutting-edge AI infrastructure, with potential for flexible arrangements depending on team structure.

AI-First Company Culture

Work within an organization that integrates frontier AI into daily engineering practices, providing unmatched exposure to emerging AI capabilities and patterns in a production environment.

Impact at Scale

Direct influence on how millions of users and enterprise customers interact with AI agents, with clear visibility between your technical contributions and product adoption.

Cross-Functional Collaboration

Work directly with product, design, data science, sales, and customer teams, expanding your perspective beyond pure engineering and developing strategic business acumen.

Interview process

1
Technical Screening Call — Initial conversation with recruiting team to discuss your background, AI agent experience, and technical depth. Prepare to discuss specific projects where you've built AI or agent-related systems and your hands-on experience with frontier models.
2
System Design Interview — Detailed technical discussion focused on designing scalable backend systems for AI agent platforms. Be prepared to discuss architectural decisions for connector systems, knowledge base integration, evaluation frameworks, and enterprise-scale deployment patterns.
3
Product and User Empathy Discussion — Conversation with product or team members to assess your ability to understand non-technical workflows, think from a product perspective, and translate business needs into technical solutions. You may discuss how you'd approach building AI solutions for specific business functions.
4
Coding Assessment or Take-Home Project — Technical coding challenge or take-home assignment focused on building AI integrations, connector systems, or backend services. Likely emphasizes Python and your ability to work with modern AI frameworks and tools.
5
Enterprise and Cross-Functional Collaboration Round — Interview with team members from different backgrounds (engineering, product, business) to assess your ability to collaborate across disciplines and understand diverse business use cases and operational challenges.
6
Leadership or Principal Engineer Discussion — Final round conversation with senior leadership covering your engineering philosophy, long-term vision for AI adoption patterns, and ability to drive technical decisions that balance innovation with user impact.