Member of Technical Staff (Software Engineer, Enterprise Knowledge Platform)
Company websiteperplexity.ai (Perplexity, opens in new tab)
Base salary $220K to $405K
Employer-published annual base salary, USD. Advertised base range in San Francisco, CA +3 more
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSan Francisco, CA +3 more
- WorkplaceHybrid
- Status at last checkSource accepting applications when checked
- Last checked (1 h ago) · observed since
All 4 locations
- San Francisco
- Palo Alto
- Seattle
- New York City
About this job
About the Role
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.
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.
Source: Perplexity on Ashby · View original posting (opens in new tab)
This listingMember of Technical Staff (Software Engineer, Enterprise Knowledge Platform)$220K to $405K
Member of Technical Staff (Software Engineer, Computer)$220K to $405K
Member of Technical Staff (Software Engineer, Enterprise AI Products)$220K to $405K
Member of Technical Staff (AI Infrastructure Engineer)$220K to $405K
Member of Technical Staff (AI Software Engineer, Agents)$220K to $405K
Product Marketing Manager, API$220K to $290K
Member of Technical Staff (Software Engineer, Backend Platform)$220K to $405K
Chart as a table
| Role | Published base |
|---|---|
| Member of Technical Staff (Software Engineer, Enterprise Knowledge Platform) (this listing) | $220K - $405K |
| Member of Technical Staff (Software Engineer, Computer) | $220K - $405K |
| Member of Technical Staff (Software Engineer, Enterprise AI Products) | $220K - $405K |
| Member of Technical Staff (AI Infrastructure Engineer) | $220K - $405K |
| Member of Technical Staff (AI Software Engineer, Agents) | $220K - $405K |
| Product Marketing Manager, API | $220K - $290K |
| Member of Technical Staff (Software Engineer, Backend Platform) | $220K - $405K |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2026-06-15
Where this pay comes from
Location as the employer wrote it: Multiple locations (4)
USD 220,000 – 405,000 · 1 YEAR · Salary
- Method
- Structured pay field published by the employer
- Checked
- Rule set
- us-base-v2
The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
