Member of Technical Staff, Perplexity
Software Engineer, Enterprise Knowledge Platform
$220-405k
+ Equity
4+ days a week in office
Conversational AI-powered search tool
Open for applications
Conversational AI-powered search tool
1001+ employees
Open for applications
$220-405k
+ Equity
4+ days a week in office
1001+ employees
Company mission
To advance the way people discover and share information.
Role
Who you are
- 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
What the job involves
- The Enterprise Knowledge 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
- This 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
- 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
Our take
As AI continues to drive changes in both business operations and day-to-day lives, Perplexity delivers an intelligent search engine powered by conversational AI, to improve search outcomes and "unlock the power of knowledge" for all.
Reaching impressive heights of 22M monthly visits, Perplexity's "ask anything" method of search is clearly a popular one. And what sets it apart from other search engines is in its answers, which are concise and fully cited, responding to the user query rather than simply providing a place in which they can scroll to find it themselves.
The company has raised substantial funding, with contributions from former execs at companies like OpenAI, Meta, and Quora, demonstrating the interest that it has drummed up in the space. Having doubled its annual recurring revenue between 2023 and 24, this investment has allowed Perplexity to continue building out its product, and accelerate growth into the future.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 9 rounds)
Sep 2025
$200m
LATE VC
Jul 2025
$100m
LATE VC
Total funding: $1.8bn
Company HQ
Financial District, San Francisco, CA
Leadership
Aravind Srinivas
(Co-Founder, President & CEO)Former Research Scientist at OpenAI, who has interned in Research at Google and Deep Mind. Obtained a PhD in Computer Science from the University of California, Berkeley.
Denis Yarats
(Co-Founder & CTO)Prior to co-founding Perplexity in 2022, Denis was an AI Research Scientist at Facebook AI Research, a Staff ML Engineer at Quora, and a Software Development Engineer at Microsoft.
Johnny Ho
(Co-Founder & CSO)Previously a Quantitative Trader at Tower Research Capital, and Engineer at Quora.
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