Senior Backend Engineer - Backend Platform (USA Only, 100% Remote)

Anywhere in the WorldFull timeRemote

About the role

Headquarters: USA - Remote URL: http://close.com 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 130-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
About the Role Close runs on Python: Flask web apps, TaskTiger, billions of MongoDB documents, a public REST API that customers and external agents both build on
Four product teams ship features on top of that every week
Backend Platform owns the foundation underneath it all
As an Engineer on our Backend Platform team, you’re building for other engineers
When a product team adds a new API endpoint, most of what they write isn't their feature — it's wiring: authentication, permissions, rate limiting, the shape the endpoint has to take so it looks like every other endpoint when you squint
Multiply that across telemetry, eventing, database migrations, authorization, and local development, and you get the problem this team exists to solve: make the paved road faster to walk than the trail
The other half of the team's mandate is the AI development platform
We've built DevDawg, cloud-based development environments preconfigured for our entire application stack and our engineering practices, and Spice, our extension layer on top of a coding agent that runs internal review workflows and root cause analysis
Spice is hooked into GitHub and can already approve mergeable pull requests on its own for simple changes
This team is responsible for expanding what it can safely handle
This is a new team
You would be one of the first hires, and you'd have unusual influence over what the team owns and how it operates
One thing to know: 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'd start, but over time you'll likely have the opportunity to work on many different projects
This role is open at the Senior and Staff levels
You don't need to pick one when you apply: we'll calibrate together during the process
You are A seasoned Python engineer
Python is our backbone and it matters in this role more than on other teams at Close
Architectural patterns don't port cleanly between languages, and you'll be setting the patterns
Go, Rust, or TypeScript alongside it is welcome
Drawn to meta-problems
This team doesn't ship features to customers; it changes how features get built
AI-native in production
You've shipped meaningful LLM-backed or agentic work to real users
For a team building AI tooling that other engineers depend on, this is foundational to the role
Working with AI in your day-to-day
You use coding agents in your own workflow and have a real POV on where they help and where they get in the way
On this team, your POV becomes the product
We fund best-in-class developer tools and treat experimentation as part of the work
Fluent in observability and production practice
Telemetry, metrics, tracing, alerting, Sentry ownership, what production-ready actually means
You've been the person who made a system legible when it broke
Opinionated about API design
You've shipped internet-facing APIs and you think about who's on the other end — apps, agents, humans reading docs — and what each needs
Battle-tested
You've debugged incidents where latency budgets didn't hold, owned a system everyone else relied on, or carried a pager for something with real customer impact
A builder first
You'd rather get a rough v1 in front of five engineers than spend three weeks on abstractions
Energized by internal customers
Your users are a Slack DM away
You'll talk to them directly instead of through a PM, and you'll use most of what you build yourself
You will Expand DevDawg and Spice
Cloud development environments for our full stack, paired with automated review and root-cause tooling that can approve real pull requests
Making that safe enough to cover more of our review surface is one of the team's biggest projects
Build the paved roads for our API layer
REST blueprints, GraphQL schema, OpenAPI, realtime, and the auth and permission plumbing that every endpoint needs
Consistent patterns are what make shared utilities possible
Modernize the backend framework layer
Web and async compute frameworks, performance, and migration paths (Flask → FastAPI, among

Required skills

Customer Support

python

fastapi

flask

typescript

javascript

sql

postgresql

aws

docker

kubernetes

sales