Staff Software Engineer, Integrations

About Team
Flow by ServiceNow is a standalone conversational AI service desk, with its own product and its own codebase. Employees ask for help wherever they already work — Slack, Microsoft Teams, email, or the browser. Flow answers from connected knowledge, handles the request, and runs automations the team publishes. AI is how the product works, and a person stays in control when the action is sensitive.
Flow is a new company inside ServiceNow, reimagining every function from first principles. We hire builders with agency and curiosity who use AI as the default way to design, review, and operate. They challenge inherited ways of working and treat their work as craft.
Flow is only as good as the systems it can read and the actions it can take. On the Integrations team, you’ll own how Flow connects to the systems companies run on, including identity, devices, ticketing, documents, and chat. You’ll turn them into three things: assets Flow can inventory, knowledge it can cite, and actions it can run. Every connection keeps each source’s permissions intact.
In your first 12 months, you’ll make sure priority systems support assets, automations, and knowledge that Flow can actually use. Connection health and failed actions will be operable without a specialist in the room, and every new system will follow one contract for permissions, audit, and proof.
Types of problems you’ll get to work on
Integration generation at scale. We don’t hand-write connectors. Build the pipeline that generates them from whatever a vendor publishes, which is often not a clean OpenAPI spec — and own spec normalization, pinned provenance, and deterministic regeneration across a growing catalog.
Tool surfaces an AI can actually call. An action’s name, description, schema, and examples are the product surface a model sees. Design them so the assistant picks the right action first try, scope them so they are safe for an AI to call, and measure whether it does.
Permissions and credentials you can prove. If a file is private to HR, a marketer must not see its title, its URL, or that it exists. Own access-model mirroring and delete detection, and pick the smallest credential policy each provider needs across validation, reconnect, revocation, and tenant isolation.
AI-native development itself. Agentic coding is how this team ships, not a productivity add-on. Author the skills and guardrails that let agents do integration work correctly, keep the deterministic provider-fake harness that makes fast iteration possible, and review generated code critically before anything ships.
Engineering leadership. Raise the technical bar through architecture decisions, code review, and coaching. Partner with product and design to shape requirements and explain tradeoffs clearly.
Our stack
TypeScript on Bun, PostgreSQL, Temporal for durable execution, Nango as the OAuth broker. We expect you to be excellent in a comparable stack and fluent in ours within weeks. We are not screening for this list.
To be successful in this role you have:
Experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This includes current, hands-on fluency with AI-assisted and agentic development.
8+ years of software engineering with strong fundamentals in system design, API design, relational data modeling, and testing.
Hands-on depth building and operating third-party integrations that real tenants depended on — webhooks, rate limits, pagination, incremental sync, delete detection.
Experience designing API or SDK surfaces that another system consumes correctly without a human reading the documentation — and interest in doing that for a model.
Ownership of a versioned contract where a rename breaks a customer: stable identifiers, deprecation, and migration.
Sound judgment about least-privilege access — scope minimization per action, admin consent, secrets handling, tenant isolation — and about which actions an AI may take on its own.
Comfort with ambiguity and the drive to build from 0–1 without a playbook.
A record of technical leadership: architecture ownership, code-quality bar-raising, and mentoring engineers.
Nice to have
Experience evaluating tool or function selection — measuring whether a model picks the right action.
Experience with RAG or the Model Context Protocol (MCP).
Familiarity with SCIM or directory sync.
Background in IT service management or employee support products.
Why join us
Intelligence is commoditizing. Connection and execution are not. A model that reasons brilliantly and cannot act on the systems a company actually runs is a demo.
Flow already reaches a broad and growing share of the systems enterprises run day to day, and this team decides how fast that surface grows and how far each connection can be trusted. We aren’t writing connectors one at a time. We’re building the generator, the contract every provider follows, and the proof that what Flow says it did, it actually did. Real problems: a vendor specification that lies, an access model that must be mirrored exactly or not at all, a sync that resumes cleanly a million items later, and a tool surface a model has to get right without reading the documentation.

Skills
TypeScript, PostgreSQL, Data Modeling, Documentation

Employment type
Full-time

Work arrangement
Hybrid