Senior Software Engineer – Backend (USA Only - 100% Remote)

About Us Since 2013, we’ve been building a CRM that gets out of your way and helps your team sell more, faster. Now we’re building AI into every part of it, so Close does the busywork and your team does the selling. No manual data entry, no 10-click workflows. Just communication-first, AI-powered sales software designed to help you succeed and scale. We're bootstrapped and profitable which means we answer to our customers and play by our rules. We're proud of our 120-person, 100% remote team, focused on building Close so that no small, scaling business fails because it can't figure out sales. Our Stack Our backend tech stack consists primarily of Python Flask web apps with our TaskTiger scheduler handling many of the backend asynchronous task processing chores. Our data stores include MongoDB, PostgreSQL, Elasticsearch, and Redis. The underlying infrastructure runs on AWS using a combination of managed services like EKS, MSK, RDS and ElastiCache and non-managed services running on EC2 instances. We have CI/CD pipelines that build Docker images, run automated tests and deploy to Kubernetes clusters. We also use these images in our local development environment allowing coding locally against all of our services. We have a well-documented public API that is consumed by our front-end JavaScript app as well as numerous integrations. Our infrastructure is heavily automated using Terraform, Ansible and other AWS tools. We love open sourcing our code and ideas on our GitHub and on The Making of Close , our behind-the-scenes Product & Engineering blog. Check out our open source projects like SocketShark , TaskTiger , LimitLion and ciso8601 . AI is both how we build and what we ship, and that's reshaped what engineering looks like. This is a transformation we’re embracing and find deeply exciting. Our Teams When you apply, you’ll have an opportunity to highlight the team that appeals to you the most. We’ll do our best to honor this preference, but we'll also be evaluating skillset fit and the needs of the business. One thing to know up front: we do move people between teams as the work shifts. Most engineers here end up on more than one team over their time at Close - this team is where you’ll start, but over time you’ll likely have the opportunity to work on many different projects. The work splits roughly like this. Pick the team that excites you most: Agents. Build Close’s agentic platform and customer-facing AI experiences. Voice Agents, Custom Agents, and Ask Chloe are already in flight. You’d work on the shared intelligence and orchestration layer that powers AI-driven processes across the product. That includes the ecosystem around our APIs and MCP surface, which enables external agents like Claude and ChatGPT to operate Close. This team is pushing deeper into applied AI, retrieval, and agent infrastructure more than ever before. Communications. Own the voice, SMS, and email infrastructure powering Close — Twilio, WebRTC, WebSockets, AssemblyAI, ElevenLabs, and more. You’ll help build our voice, messaging, and conversational AI agents while scaling real-time AI across calls, email, and calendar sync infrastructure. CRM. Build the structured context layer that humans and agents both depend on. We're rebuilding the data model so it flexes with real businesses, making the CRM agent-ready (retrieval, traversal, action coverage through MCP and our public API), and shipping the next generation of AI-native CRM features (AI Enrich, Autofill, AI Search). Operating on billions of Mongo documents with Elasticsearch underneath. Growth. Run the experiments and own the billing infrastructure that turn trials into paid customers. Stripe metered billing, AI credit top-ups, the activation and conversion funnel, and the team's biggest bet right now: agentic onboarding (an agent that walks new customers through setup, configuration, and first comms). Hypothesis-driven, metric-first. You Are A seasoned engineer. Python is our backbone, but perhaps you’ve worked across Go, Rust, or TypeScript. You pick the right tool for the problem rather than retreating to what you know. You’ve seen a variety of problems, can collaborate and self-direct. Building AI. You've shipped LLM-backed features for real users and have a POV on where they're trustworthy, where they fall over, and how to get them production-ready when customers bet revenue on the output. We use Pydantic, Temporal, LangFuse, and other modern AI infrastructure tooling to power our ag