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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Developer Velocity - **Company:** Hex - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Cloud Computing, Cloud Engineering, Databases, Continuous Integration, Cursor, Programming Tools, Python (Programming Language), PostgreSQL, Node.Js, Redis, SQL Databases, Systems Architecture, TypeScript, WebSocket, Datadog, Data Logging, ReactJS, Delivery Pipeline, Large Language Models, Claude Code, Caching, AI Coding Agents, Backend, Agentic-AI, Kubernetes, Free and Open-Source Software, Graphql, Data Management, Front End Software Development, React Redux, Software Coding, Terraform, Docker, Monolithic Repository - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25197_6058121fa3a3dc478ab56f587b4e9c3e-2354041876 ## About the Role * 5+ years in developer productivity, build/release engineering, or internal platforms. Experience building the tools and systems the whole engineering org depends on daily. This is the core of the role. * Deep build-systems and CI/CD expertise. Repo build tooling and caching, CI pipelines, and safe deployment workflows. * Breadth across the stack. You can reason from backend application code through infrastructure-as-code (e.g. Scalr / Terraform), deployment, and observability (e.g. Datadog), and follow a change all the way from a local build to production. * Strong proficiency in TypeScript/Node.js and SQL; Python a plus. * Comfortable working in backend systems, APIs, and distributed or asynchronous workflows that multiple teams rely on. * Hands-on experience with LLMs and agent frameworks - tool calling, MCP, context management, orchestration, failure handling. * Deep personal fluency with AI coding tools and well-formed opinions about where they break down. * Platform mindset: you build for adoption and self-service, treat internal engineers as customers, and would rather ship a good default than write a style guide. * Solid AWS / Docker / Kubernetes fundamentals. * Excellent written communication. Much of your leverage are docs, templates, and examples that scale beyond the conversations you're in. * Comfort with ambiguity; this space invalidates its own best practices every few months., * Experience with agent systems or AI-assisted development at scale. * Background in large-scale system architecture. * Familiarity with repository tooling and practices. * Hands-on with Hex's stack: GraphQL, WebSockets, Postgres, Redis, BullMQ/Temporal, Turborepo and Terraform/Helm on AWS. * Observability tooling or data-platform experience. * Experience with Python * Hands-on experience with GraphQL, WebSockets, BullMQ, or Temporal * Familiarity with observability tooling (metrics, tracing, logging) * Background in data platforms, developer tools, or collaborative systems * Experience with frontend technologies (e.g. React, TypeScript) for end-to-end context * Open-source contributions, especially in backend or infrastructure projects ## Description Hex is an AI native company and we are constantly playing with the latest models and tools. We are looking for a new team member to help us drive our developer tooling and systems to the cutting edge. This role has two equally important halves. First, the classic developer-productivity foundation: fast builds, reliable CI/CD, reproducible dev environments, building the systems the whole engineering organization depends on every day. Second, the AI layer on top of it: as coding agents get more capable, the hard problems move beyond writing code to context, environments, validation, and safe integration into production. You'll build the systems that let coding agents operate with deep awareness of Hex's codebase, infrastructure, and development lifecycle. This is role that is intended to drive impact organization-wide. You'll partner with teams across engineering to understand how they work, build the shared systems that raise everyone's velocity, and establish the standards and guardrails that keep a fast-moving org safe. Success looks like shipping tools, measuring it, and teams adopting what you build., + Own the internal developer platform end to end. Architecture, build, rollout, operation, and measurement. + Invest in build and CI/CD. Monorepo build tooling and local/remote caching, CI pipelines, merge queues, fast and reliable test suites, and safe rollout/deploy mechanics for the whole org. + Build great dev environments. Collaborate across teams to find the best patterns. Fast local builds and caching, reproducible multi-platform cloud development environments. + Raise org-wide velocity. Establish metrics and dashboards to help the teams steer the ship and partner with teams across engineering to find and remove the biggest bottlenecks. Examples such as cycle time, review latency, and change failure rate. AI developer experience * Build the agent context layer. Repo-level context infrastructure (agent guidance conventions, architecture and domain context), and shared skills/subagents/hooks that make agents competent in Hex's codebase. * Evaluate and integrate new third-party coding tools (the next Claude Code, the next Cursor Cloud Agent and future tools) and make them work against our codebase and other org tools (Slack, Notion, Linear, etc). * Across both + Drive adoption through developer experience. Set up good defaults, clear docs, low friction and embed with teams to generalize their wins into shared platform capabilities. + Own the safety layer. Work with our teams to create permissions, secrets handling and audit trails., Our product is a web-based notebook and app authoring platform. Our frontend is built with Typescript and React, using a combination of Apollo GraphQL and Redux for managing application state and data. On the backend, we also use Typescript to power an Express/Apollo GraphQL server that interacts with Postgres, Redis, and Kubernetes to manage our database and Python kernels. Our backend is tightly integrated with our infrastructure and CI/CD, where we use a combination of Terraform, Helm, and AWS to deploy and maintain our stack.