Scale Engineer

Member of Technical Staff (Software Engineer, Acceleration)

Staff Engineer · Staff · Full Time

San FranciscoUSD 250k – 405k1mo ago
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Role

What you'll do.

Member of Technical Staff at Perplexity AI is a hybrid infrastructure and AI product engineering role focused on accelerating company velocity through strategic deployment of frontier AI capabilities. This position combines hands-on platform engineering, developer experience optimization, and applied AI research to help Perplexity scale its technology and operations. The ideal candidate brings 5-15+ years of internet-scale engineering experience, deep proficiency with AI tools and models, and the ability to reengineer systems and processes to harness cutting-edge AI technologies across the organization.

Responsibilities

  • Identify and Execute High-Impact AI Acceleration Opportunities: Conduct systematic analysis of workflows and processes across engineering, research, and business operations to identify the highest-potential opportunities for leveraging frontier AI models and agents. Prioritize initiatives based on potential impact to delivery cadence and user experience, then execute end-to-end implementations from prototyping through production deployment.
  • Make Systems and Codebases AI-Legible: Architect and implement infrastructure improvements that enhance the legibility of codebases, systems, and knowledge repositories to AI models and agents. This includes refactoring code organization, improving documentation standards, and creating structured data representations that enable AI to understand and manipulate systems more effectively.
  • Balance Quality and Velocity Through AI Leverage: Work with engineering leadership and cross-functional teams to identify the optimal equilibrium point on the quality-velocity Pareto frontier. Use AI tools and agents to shift this frontier outward, enabling the organization to maintain or improve quality standards while accelerating time-to-market for new features and products.
  • Champion AI Tool Adoption and Cultural Transformation: Stay current with emerging AI models and tools, integrate them into daily engineering workflows, and demonstrate best practices across the organization. Mentor technical staff on effective AI usage patterns, troubleshoot integration challenges, and advocate for architectural decisions that future-proof the company's infrastructure for increasingly capable AI agents.
  • Cross-Disciplinary Technical Collaboration: Build deep working relationships with frontend, backend, ML, infrastructure, and applied research teams to understand their unique technical constraints and opportunities. Translate domain expertise into collaborative AI acceleration projects that compound benefits across multiple teams and improve overall company effectiveness.
  • Build Prototypes and Production Systems: Design and develop both experimental prototypes and scalable production systems that demonstrate AI-powered improvements to company processes. This includes implementing developer tools, infrastructure automation, code generation systems, and process automation that serve as concrete examples of effective AI integration.
  • Strategic Technology Decision-Making: Evaluate emerging technologies and frameworks relevant to AI product development and infrastructure engineering. Make high-conviction decisions about technology bets in areas where indecision might otherwise delay progress, establishing technical direction for infrastructure choices that support next-generation AI capabilities.
  • Talent Acquisition and Culture Building: Partner with engineering leadership and recruiting teams to identify and attract engineers who thrive in AI-forward technical environments. Contribute to defining hiring criteria that emphasize AI-native thinking and hands-on technical capability, while helping shape company culture to emphasize continuous learning and experimental approaches.

Qualifications

What we look for.

Technical

  • Production-Scale Backend Development

    Demonstrated ability to design, build, and maintain backend systems handling significant scale and complexity. Experience with distributed systems, microservices architecture, or service-oriented architecture in production environments.

  • Infrastructure and Platform Engineering

    Substantial hands-on experience architecting, building, or optimizing infrastructure platforms that abstract complexity for other engineering teams. This includes experience with cloud infrastructure (AWS, GCP, or similar), containerization, orchestration, or internal developer platforms.

  • Python Proficiency

    Strong capability in Python for backend development, infrastructure tooling, and automation. Experience building production systems and tools in Python at scale.

  • AI Models and LLM Integration

    Hands-on experience integrating large language models and AI agents into production systems. Understanding of prompt engineering, fine-tuning approaches, vector databases, retrieval-augmented generation (RAG), and agent frameworks.

  • API Design and Integration

    Experience designing and consuming REST, gRPC, or other API protocols. Understanding of API versioning, backwards compatibility, and cross-service communication patterns in distributed systems.

  • Software Architecture and System Design

    Ability to evaluate architectural tradeoffs, design systems for scalability and maintainability, and communicate complex technical decisions across teams. Experience with both greenfield and brownfield system modernization.

  • Developer Experience and Tooling

    Track record of building or improving developer tools, frameworks, or infrastructure that measurably improve engineering productivity. Understanding of what makes tools intuitive and adoption challenges.

Education

  • Bachelor's Degree in Computer Science or Related Field

    Formal computer science education or equivalent hands-on experience demonstrating strong fundamentals in algorithms, data structures, systems design, and software engineering principles.

Experience

  • 5-15+ Years of Industry Experience

    Substantial experience in software engineering roles, with particular emphasis on positions involving infrastructure, platform engineering, or developer acceleration. Experience at internet-scale companies or with systems serving millions of users strongly preferred.

  • Infrastructure or Platform Engineering Focus

    Significant portion of career dedicated to building platform abstractions, developer tools, or infrastructure systems in technically complex environments. This demonstrates familiarity with long-term technical debt management and scaling challenges.

  • Cross-Functional Technical Leadership

    Evidence of collaborating effectively with diverse technical teams (frontend, backend, ML, research) to solve complex problems. Experience navigating technical decisions where multiple valid approaches exist.

  • AI Tool Integration Experience

    Daily hands-on usage of contemporary AI tools and models (LLMs, code generation tools, agents) in professional work. Demonstrated understanding of both capabilities and limitations of frontier models.

Skills

Required

  • Python

    Production-grade backend development and infrastructure automation using Python. Proficiency with popular frameworks and libraries for systems development.

  • Distributed Systems Design

    Design and implementation of distributed systems, microservices, and cloud-native architectures. Understanding of consistency models, fault tolerance, and performance optimization.

  • LLM Application Development

    Hands-on ability to integrate large language models into applications, including prompt engineering, RAG systems, agent frameworks, and production deployment considerations.

  • Cloud Infrastructure

    Practical experience with cloud platforms (AWS, GCP, Azure) including compute, storage, networking, and observability services. Infrastructure-as-code and deployment automation experience.

  • System Architecture and Design

    Ability to design complex systems with consideration for scalability, performance, reliability, and maintainability. Experience making and communicating architectural tradeoffs.

  • Developer Tools and Platform Design

    Experience building or significantly improving developer-facing tools, SDKs, or internal platforms. Understanding of developer experience principles and adoption patterns.

  • Cross-Team Collaboration

    Demonstrated ability to work effectively with engineers from different specialties (ML, frontend, backend, research) and understand their technical constraints and priorities.

Preferred

  • Rust

    Nice to have

    Experience with Rust for systems programming, performance-critical components, or infrastructure tooling. Understanding of Rust's memory safety guarantees and when they provide value.

  • TypeScript/Node.js

    Nice to have

    Full-stack development experience with TypeScript and Node.js. Familiarity with modern frontend-backend integration patterns and type-safe API design.

  • Go

    Nice to have

    Systems programming or infrastructure tool development in Go. Understanding of goroutines, concurrency patterns, and Go's approach to distributed systems.

  • Applied Machine Learning Infrastructure

    Nice to have

    Understanding of ML pipelines, model serving infrastructure, or CUDA/GPU programming concepts. Knowledge of frameworks like PyTorch, TensorFlow, or emerging AI infrastructure tools.

  • Vector Databases and Search

    Nice to have

    Experience with vector databases (Pinecone, Weaviate, Milvus), embeddings, similarity search, and retrieval-augmented generation implementations.

  • Agent Frameworks

    Nice to have

    Practical experience with AI agent frameworks (LangChain, AutoGPT, Crew.ai, etc.). Understanding of agentic design patterns and multi-step reasoning implementations.

  • Kubernetes and Container Orchestration

    Nice to have

    Production experience deploying, scaling, and managing containerized applications using Kubernetes or equivalent orchestration platforms.

  • Observability and Monitoring

    Nice to have

    Design and implementation of comprehensive monitoring, logging, and tracing systems for complex distributed applications. Familiarity with tools like Datadog, New Relic, or open-source alternatives.

  • Process Automation and Workflow Engineering

    Nice to have

    Experience designing and implementing automation frameworks that orchestrate complex workflows across technical and business teams, potentially including human-in-the-loop components.

  • Internet-Scale Infrastructure Experience

    Nice to have

    Prior experience at companies known for large-scale systems (FAANG, unicorn startups, infrastructure specialists). Exposure to problems and solutions at 100M+ user scale.

Tech stack

Languages

PythonTypeScriptRustGo

Frameworks

LangChainFastAPIDjango/FlaskReact/Next.jsPydantic

Databases

PostgreSQLRedisVector Databases (Pinecone/Weaviate/Milvus)MongoDB

Tools

KubernetesDockerAWS/GCP/AzureGitHub/GitLabTerraform/CloudFormationDatadog/New Relic

Other

OpenAI API / Anthropic ClaudeRetrieval-Augmented Generation (RAG)AI Agents and Multi-Step ReasoningCUDA / GPU Computing FundamentalsPrompt Engineering and Fine-Tuning

Compensation

Pay and benefits.

Base·USD 250,000 – 405,000

Full posting

Original listing.

About Perplexity AI

Perplexity is an AI-powered answer engine built to serve the world’s curiosity with fast, trustworthy answers grounded in the live web and backed by clear citations. It combines multiple leading models with real-time search to synthesize up-to-date, source-linked responses instead of traditional search results. On top of this foundation, Perplexity is rolling out Computer, a general-purpose AI worker that can use software like a human to research, build, and execute end-to-end workflows for users.

About the Role

Perplexity is seeking creative, AI-pilled engineers to join our Acceleration team. Our company is reshaping the way people navigate the internet and the wider world. The Acceleration team's mandate is to continually up-level the way our company and our teams operate, to deliver order-of-magnitude improvements in product and user experience.

Just as Perplexity is heralding an agentic future for the internet, you will ensure that our company makes the best use of AI tools and agents to multiply the leverage of our lean, mission-focused teams. Today, Perplexity uses AI tools across everything we do, from frontend and backend engineering to applied AI research and business operations. Tomorrow’s AI technology will be even more capable, and the Acceleration team will lead both software and process engineering to harness AI so that the company can deliver at maximum velocity.

The ideal candidate will possess sound technical judgment forged in the trenches of internet-scale companies, paired with voracious use of today’s most advanced AI tools/models and a capacious imagination to rethink how work gets done in the leading companies of tomorrow. They should thrive in a hybrid role that marries facets of AI product development, infrastructure/platform engineering, developer experience, and more.

Key Responsibilities

  • Identify, prioritize, and execute on the highest-potential opportunities to accelerate Perplexity’s delivery cadence with frontier AI capabilities.

  • Develop empathy for the nuances of our technical and business teams’ work across disciplines and verticals, toward collaborating with them to harness AI in new ways.

  • Make our codebases, systems, and knowledge stores legible to AI.

  • Help the company triangulate toward the optimal point on the quality-velocity Pareto frontier, while shifting that frontier outward with AI tools and agents.

  • Keep abreast of new AI tools/models and make maximally effective use of them in your own work, setting an example across the company and inspiring others to do the same.

  • Help the company make high-conviction bets on specific technologies in situations where indecision is a comfortable and tempting default.

  • Work with engineering leadership, recruiting, and others to ensure we instill an AI-forward culture and select for those who will thrive in such an environment.

    Note: this is an extremely technical and hands-on role. Strong vision and creativity is necessary but not sufficient for success. You will be expected to build both prototypes and production systems, as well as to reengineer our codebases, infrastructure, processes, and anything else that must evolve to realize this vision.

Qualifications

  • 5 to 15+ years of industry experience, ideally with a substantial portion focused on infrastructure/platform engineering or developer acceleration in environments featuring significant technical complexity.

  • Daily use of AI models/tools, along with strong intuition on their jagged frontiers.

  • Proficiency with Python, along with experience and/or desire to learn more about Rust, TypeScript, Go, and other languages.

  • Strong understanding of (and empathy for) the work of technical staff across disciplines, along with an enthusiasm to learn about the work of business and operations teams.

  • Broad working knowledge of the typical stacks used by AI product & applied research companies. (For example: you don’t need to have experience hand-rolling CUDA kernels, but you should know what CUDA is and when/where/why it’s used.)

  • An obsession with improving the legibility of (and reducing brittleness in) both software and human-orchestrated processes.

  • Experience navigating tricky exploration-exploitation tradeoffs, balancing the need to ship today with the need to build capacity for tomorrow.

If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.

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