ClickUp

Senior Software Engineer, Internally Deployed Products

Senior · Удалённо · США · Английский B2

Навыки

  • AI-агенты
  • API (интеграции)
  • AWS
  • CRM-системы
  • ETL / ELT
  • Google Cloud
  • GitHub
Ещё 17
  • Google Tag Manager
  • HR-процессы
  • IAM / IDM
  • Jaeger / OpenTelemetry
  • LLM
  • Machine Learning
  • Room / Realm / CoreData
  • Node.js
  • OAuth / OIDC
  • Мониторинг и observability
  • Prompt engineering
  • Python
  • React
  • Смарт-контракты
  • SRE-практики
  • Terraform
  • TypeScript

О компании и продукте

  • The Foundry is ClickUp's internal AI innovation lab — embedded inside GTM Systems and accountable for turning AI capabilities into production-grade, internally deployed products that make every GTM function faster and smarter. We build the infrastructure that powers AI-first work across Sales, Marketing, Post-Sales, and Revenue Operations.
  • As the Senior Software Engineer on this team you will own the technical delivery of our MCP server platform, agent orchestration layer, and internal tooling — shipping production systems used daily by hundreds of ClickUp employees, and scaling your own throughput by treating AI tools as first-class engineering collaborators.
  • At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀

Задачи

  • MCP SERVER PLATFORM
  • Design, build, and operate Model Context Protocol servers that expose CRM, ticketing, analytics, and communication data to AI agents across the GTM stack
  • Implement Okta PKCE authentication flows and RBAC policy enforcement so agents access only the data they're authorized to touch
  • Maintain deployment infrastructure on AWS (Bedrock, Lambda, ECS, API Gateway) and contribute to GCP workloads where applicable
  • Own observability: structured logging, distributed tracing, latency SLOs, and on-call runbooks for every production server
  • AGENT ORCHESTRATION & AI-NATIVE PRODUCTS
  • Build and maintain multi-step autonomous agents that execute end-to-end GTM workflows — lead qualification, deal room assembly, onboarding automation, support triage, and more
  • Architect prompt engineering frameworks, tool-call schemas, and agent evaluation harnesses that make AI behavior predictable and auditable
  • Integrate with LLM providers (Anthropic, OpenAI, AWS Bedrock AgentCore) and maintain version-pinned, cost-tracked model configurations
  • Deliver AI-powered internal applications (web apps, CLI tools, Slack integrations) that non-technical GTM stakeholders use without friction
  • GTM PLATFORM ENGINEERING
  • Own full-stack feature delivery across TypeScript/Node.js backends and React/TypeScript frontends for internal tooling
  • Write Python automation scripts, ETL pipelines, and data transformation layers that feed GTM analytics and AI context
  • Collaborate with Systems Engineering and GTM Engineering teams on cross-cutting API standards, data contracts, and integration patterns
  • Conduct code reviews, establish engineering standards, and actively mentor junior engineers toward higher leverage
  • AI-NATIVE DEVELOPMENT PRACTICE
  • Use AI coding assistants (Claude, Cursor, GitHub Copilot) as primary engineering accelerators — not supplements — to ship at a pace that punches above a single engineer's weight
  • Document AI usage patterns, prompt templates, and agentic workflows so the team's collective throughput compounds
  • Stay current on MCP protocol evolution, agent frameworks (LangGraph, CrewAI, custom), and emerging LLM capabilities
  • bring back what matters

Требования

  • 5+ years of professional software engineering experience with production systems
  • Expert-level TypeScript and Node.js — idiomatic, typed, testable server-side code
  • Strong Python — automation scripts, data pipelines, and scripting for AI/ML tooling
  • Meaningful AWS deployment experience: Lambda, Bedrock, ECS/Fargate, API Gateway, IAM, Secrets Manager, CloudWatch
  • Demonstrated experience integrating with LLM APIs (OpenAI, Anthropic, AWS Bedrock, or equivalent) and shipping AI-powered features to real users
  • Solid foundation in REST API design, OAuth 2.0 / OIDC authentication, and secure credential management
  • Experience with CI/CD pipelines, infrastructure-as-code (Terraform, CDK, or SAM), and cloud cost awareness
  • Clear written communication: design docs, ADRs, and runbooks that others actually read
  • Track record of using AI tools (LLM assistants, copilots, agentic workflows) as a genuine productivity multiplier — not a gimmick
  • STRONGLY PREFERRED
  • Hands-on experience with MCP (Model Context Protocol) — building servers, defining tool schemas, or operating multi-server agent environments
  • GCP experience (Cloud Run, BigQuery, Cloud Functions) to complement AWS work
  • Salesforce, HubSpot, or other CRM API integration work — understanding GTM data models is a significant advantage
  • React or Next.js front-end capability to ship internal dashboards and tooling without hand-offs
  • Familiarity with agent orchestration frameworks: LangGraph, AutoGen, CrewAI, or custom orchestration patterns
  • Experience in a GTM Systems, RevOps, or Sales Engineering context
  • Okta / identity provider integration work (PKCE flows, SCIM, token management)
  • HOW WE OPERATE
  • The Foundry runs on three principles that every team member is expected to embody
  • Ownership — You own the full lifecycle of what you build: design, delivery, observability, and iteration. We don't have a separate ops hand-off
  • Velocity — We ship to production in days, not quarters. AI tools are how we maintain that pace without sacrificing quality
  • Atomicity — Every deliverable is a discrete, composable unit — an MCP server, an agent, an API. Nothing we build requires everything else to work
  • WHO THRIVES HERE
  • This role is a strong fit if you are genuinely excited to work at the intersection of AI infrastructure and internal product — not just as a user of AI, but as someone building the layer that makes AI useful for an entire go-to-market organization
  • You are comfortable with ambiguity, you default to shipping, and you see every piece of internal tooling as a product that deserves operational discipline

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