Member of Technical Staff (Software Engineer, Connector Platform)
Backend Engineer · Senior · Full Time
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Role
What you'll do.
The Connector Platform team at Perplexity AI is seeking a Member of Technical Staff (Software Engineer) to design and build the critical data layer that enables AI agents to reliably access and execute integrations across hundreds of heterogeneous systems. This role requires deep expertise in backend systems architecture, API platform design, and authentication/authorization patterns to create a unified, typed interface for agent-driven enterprise integrations. You'll own end-to-end responsibility for connector runtime, semantic layers, tool discovery, robustness patterns, and quality frameworks that make agents more effective by providing grounded, permissioned access to real-time data and enterprise systems.
Responsibilities
- Connector Runtime Architecture and Implementation: Own the complete design, implementation, and evolution of the connector runtime system that registers, hosts, and executes diverse connector types (native, MCP servers, CLI-backed tools) behind a unified agent-facing interface. Build robust registration mechanisms, execution orchestration, and lifecycle management while maintaining type safety and backward compatibility.
- Semantic Layer and Knowledge Management: Architect and expand the semantic layer including tool schemas, entity schemas, capability metadata, and relationship modeling. Implement mechanisms for capturing and applying organization- and account-specific corrections and knowledge, ensuring the platform becomes the source of truth for institutional knowledge rather than fragmenting it across individual connectors.
- Tool Discovery and Selection Optimization: Design and implement the tool-discovery and tool-selection surfaces that agents use to identify and invoke appropriate connectors. Optimize for both model accuracy in tool selection and context efficiency, enabling agents to make intelligent decisions about which tools to call and when, directly improving agent reasoning quality.
- Agent Loop Robustness and Reliability: Implement comprehensive robustness patterns including structured result formatting, partial-failure semantics, intelligent retry logic, idempotency guarantees, pagination handling, rate-limit management, and deep observability into every agent-initiated tool call. Ensure agents can handle gracefully degrade and recover from integration failures.
- Authentication and Authorization Framework: Define and implement comprehensive authentication, authorization, and credential-isolation patterns for connectors including OAuth flows, BYOK (Bring Your Own Key) support, and per-org/per-account credential boundaries. Partner closely with Security and Backend Platform teams on defense-in-depth strategies to protect sensitive credentials and enforce proper access controls.
- Connector Onboarding and Quality Assurance: Build the end-to-end connector onboarding pipeline including schema specifications, test fixtures, and comprehensive evaluation suites. Establish measurable quality standards and metrics that validate connector functionality within actual agent loops, replacing hope-driven deployment with data-driven quality gates and continuous validation.
- Reliability, Observability, and Incident Response: Set and maintain the technical bar for connector reliability and operability by defining SLAs, implementing comprehensive monitoring and observability, establishing error-rate baselines, and building incident response playbooks. Ensure the high-fan-out integration surface remains always-on, performant, and predictable for mission-critical agent operations.
- Cross-functional Platform Strategy and Collaboration: Partner with product, AI, and security teams to define clear connector interfaces, establish integration patterns, and drive platform evolution. Collaborate to ensure new agent capabilities can reliably build on the shared platform while maintaining backwards compatibility and consistent developer experience.
Qualifications
What we look for.
Technical
Backend Systems Design and Implementation
Demonstrated experience designing and building production-grade backend systems at scale. Strong track record of architecting efficient, reliable, and scalable systems that handle operational complexity, with particular strength in platform-style systems serving heterogeneous downstreams.
API Integration and Platform Architecture
Hands-on expertise building or working extensively with API gateways, integration platforms, or connector systems that abstract multiple heterogeneous backends. Experience with managing API versioning, backward compatibility, and evolving interfaces without breaking clients.
Production Backend Languages
Strong proficiency in at least one statically-typed or performance-critical backend language (Python, Go, or Rust). Ability to work effectively in multi-language environments and make pragmatic language selection decisions based on technical requirements and team capabilities.
Cloud Infrastructure and Kubernetes
Hands-on experience with modern cloud infrastructure platforms such as AWS, Google Cloud, or Azure. Practical knowledge of containerization, Kubernetes orchestration, service mesh patterns, and operational best practices for running distributed systems in production.
Security Patterns and Protocol Implementation
In-depth expertise in at least one of: OAuth 2.0/OIDC protocol implementation, authorization frameworks, credential management systems, or secure secret storage. Comfort working in security-sensitive areas and making informed trade-offs between safety guarantees, implementation simplicity, and development velocity.
Education
Computer Science Foundation
Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or equivalent professional experience demonstrating deep foundational knowledge of algorithms, data structures, and systems design principles.
Distributed Systems Knowledge
Strong theoretical and practical understanding of distributed systems concepts including consistency models, eventual consistency, idempotency, failure modes, and recovery strategies.
Experience
Backend Systems Engineering
Minimum 4+ years of professional experience designing and building backend systems deployed in production environments. Mid-level candidates typically have 4-6 years; senior engineers and staff engineers should demonstrate proportionally deeper impact and architectural leadership.
API and Connector Integration
Demonstrated experience building, maintaining, or significantly contributing to API integration systems, connector frameworks, or MCP (Model Context Protocol) server implementations. Familiarity with the operational and semantic challenges of connecting diverse third-party systems.
LLM-Based Agent Tooling and Evaluation
Experience building tooling, evaluation frameworks, or supporting infrastructure for large language model-based agents. Familiarity with how agents discover, reason about, and invoke tools, and the systems-level considerations that make agent loops robust and effective.
Distributed Systems and Operational Excellence
Track record of building systems that operate reliably at scale with attention to observability, error handling, rate limiting, pagination, and graceful degradation. Experience setting and maintaining SLAs for complex production services.
Skills
Required
Backend System Architecture
Design and implement scalable, reliable backend systems with deep understanding of trade-offs between consistency, availability, performance, and operational complexity.
API Design and Integration Patterns
Expert-level API design encompassing schema evolution, backward compatibility, versioning strategies, and creating intuitive interfaces for heterogeneous consumer needs.
Python, Go, or Rust
Professional-level proficiency in at least one statically-typed or performance-critical backend language, with ability to write maintainable, well-tested, performant code.
Cloud Infrastructure Operations
Hands-on proficiency with AWS, Kubernetes, containerization, infrastructure-as-code, monitoring, and operational best practices for distributed systems.
Authentication and Authorization
In-depth understanding of OAuth 2.0, OpenID Connect, JWT, API key management, role-based access control (RBAC), and credential isolation patterns in multi-tenant systems.
System Observability and Debugging
Expert ability to implement comprehensive logging, metrics, tracing, and alerting to enable rapid debugging of complex distributed systems in production.
Pragmatic Problem-Solving
Ability to navigate ambiguity, balance competing engineering concerns (safety, simplicity, velocity), and make informed trade-off decisions alongside experienced team members.
Preferred
MCP (Model Context Protocol) Experience
Nice to haveHands-on experience developing, deploying, or managing MCP servers or understanding the protocol and its integration patterns with AI systems.
LLM Agent Development
Nice to haveExperience building or contributing to frameworks, tools, or evaluation systems for large language model-based agents, function calling, or tool use patterns.
Third-Party API Integration at Scale
Nice to haveProduction experience integrating and managing many heterogeneous third-party APIs, handling API versioning, rate limits, and maintaining reliability across diverse upstream services.
Schema and Semantic Modeling
Nice to haveExperience designing flexible, evolvable schema systems, semantic models, or metadata frameworks that enable tooling and automation across complex domains.
Type System Design
Nice to haveFamiliarity with building well-typed abstractions, potentially including experience with language-level type systems or schema definition languages (TypeScript, Protocol Buffers, GraphQL).
Security-First Engineering
Nice to haveTrack record of building security-sensitive systems with attention to threat modeling, defense-in-depth principles, and secure secret management practices.
Evaluation Framework Development
Nice to haveExperience designing and building evaluation, testing, or quality assurance frameworks that provide quantitative confidence in system behavior across diverse scenarios.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 220,000 – 405,000
Equity·Stock options
Process
Interview steps.
- 01
Initial Screening Call
Preliminary conversation with a recruiter to discuss background, motivation for the role, and alignment with the Connector Platform team's mission. Typically 30 minutes and serves to confirm basic qualifications and cultural fit.
- 02
Technical Architecture Interview
Deep-dive system design conversation with a senior engineer focused on evaluating your backend architecture skills, approach to designing scalable systems, and experience with API integration or platform problems. Expect discussion of past projects, trade-offs, and design philosophy.
- 03
Backend Implementation and Coding
Hands-on technical interview assessing your proficiency in your chosen backend language (Python, Go, or Rust). Typically includes building a small service, implementing integration patterns, or solving a design problem with code.
- 04
Security and Infrastructure Knowledge
Technical interview covering authentication patterns, authorization frameworks, credential management, cloud infrastructure, and operational considerations for production systems. May include discussing OAuth flows, secret management, or infrastructure-as-code approaches.
- 05
LLM and Agent Context Discussion
Conversation exploring your understanding of large language models, AI agent frameworks, tool use patterns, and how system architecture decisions impact agent effectiveness and reliability. No specific prior LLM experience required; willingness to learn is key.
- 06
Team and Cross-functional Collaboration
Interview with potential teammates or cross-functional partners from product, security, or AI teams assessing collaboration style, communication clarity, and ability to navigate ambiguous problems alongside experienced engineers.
- 07
Final Discussion with Leadership
Opportunity to discuss role expectations, career growth, impact potential, and answer questions with the engineering manager or tech lead overseeing the Connector Platform team.
Full posting
Original listing.
About the Role
The Connector Platform team builds the data layer that lets Perplexity's agents reach into the world's software. This team owns the systems that turn hundreds of heterogeneous integrations (native, MCP, CLI, first-party, and third-party APIs) into one unified, reliable, well-typed surface that agents can call with confidence.
The connector platform is the core layer that forms the knowledge layer for Computer: it is how the agent discovers what tools exist, understands what each one means, decides which to call, and grounds its reasoning in real, permissioned, up-to-date enterprise data. We maintain a knowledge layer above connectors that pushes and pulls context into them, rather than letting each connector hoard org knowledge on its own, making Computer the source of truth for institutional knowledge. Models are commoditizing; grounded, actionable, permissioned access to a customer's real systems is not. When this layer is fast, accurate, and semantically rich, every agent built on top of it gets smarter; when it is weak, no amount of model quality compensates.
Key Responsibilities
Own the design and implementation of the connector runtime, the system that registers, hosts, and executes built-in connectors, hosted MCP servers, and CLI-backed tools behind a single agent-facing interface.
Build and extend the semantic layer: tool and entity schemas, capability metadata, relationship modeling, and the mechanisms for capturing and applying organization- and account-specific corrections and knowledge.
Design the tool-discovery and tool-selection surfaces that agents use to find the right connector and call it correctly, optimizing for both model accuracy and context efficiency.
Make agent loops robust: structured results, partial-failure and retry semantics, idempotency, pagination, rate-limit handling, and observability into every tool call an agent makes.
Define authentication, authorization, and credential-isolation patterns for connectors (OAuth flows, BYOK, per-org credential boundaries), partnering with Security and Backend Platform on defense-in-depth.
Build the connector onboarding path (schemas, fixtures, and evaluation suites) so new connectors ship with measurable quality rather than hope, and drive the eval metrics that tell us a connector actually works inside agent loops.
Set the technical bar for connector reliability and operability: SLAs, observability, error-rate monitoring, and incident response for an always-on, high-fan-out integration surface.
Partner with product and AI teams to define clear connector interfaces and integration patterns so new agent capabilities can reliably build on the shared platform.
Qualifications
Experience designing and building backend systems that run in production (typically 4+ years for mid-level, more for senior and staff).
Strong system design skills, with a track record of building efficient, reliable, and scalable architectures, ideally including API integration, gateway, or platform-style systems with many heterogeneous downstreams.
Strong proficiency in at least one backend language such as Python, Go, or Rust, and the ability to work effectively in a multi-language environment.
Hands-on experience with modern infrastructure (for example AWS, Kubernetes, and related cloud technologies).
Depth in at least one of: OAuth and authorization protocols, API/connector or MCP-server development, schema and semantic modeling, or building tooling and evaluation for LLM-based agents.
Comfort working in security-sensitive areas (auth, authorization, credential isolation) and making pragmatic trade-offs between safety, simplicity, and velocity.
Collaborative mindset and eagerness to solve hard, ambiguous problems alongside other experienced engineers.
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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