Senior SDE - Platform

About HighLevel:HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes. To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.

Our people With over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.

Our impact Every month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that. Learn more about us on our YouTube Channel or Blog Posts   Role Summary: We’re hiring a Senior Full Stack Engineer to design, build, and scale the backend systems and frontend interfaces that power the Threads & Composer experience inside HighLevel Conversations. You’ll spend most of your time on backend and API design, data flows, and performance — while still owning the Vue 3 UI layer end-to-end. Your work will touch critical paths that process billions of messages per month, demanding reliability, throughput, and correctness at scale.   Team & System Overview: The Conversations organization powers multi-channel communication across SMS, Email, WhatsApp, and DMs — handling over 2B messages each month. The Threads & Composer team owns the inbox and composer surfaces, and the backend services that deliver and store those interactions.You’ll work across Node.js microservices, GCP-based infra (GKE, Pub/Sub, Cloud Tasks), and MongoDB/Firestore storage layers, ensuring every message and UI state stays consistent, fast, and recoverable.

Responsibilities:: Design and build the platform components Workflows and Conversations run on, queueing pipelines, storage access layers, caching strategies, rate limiters, schedulers

Own your components end to end: architecture, design docs, code, tests, rollout, dashboards, and production health

Go deep on data stores at scale , schema and index design, sharding strategies, replication behavior, and migrations on live systems with billions of records

Work close to the infrastructure : GKE autoscaling, resource tuning, Pub/Sub and Cloud Tasks topologies, and the capacity planning that keeps headroom ahead of growth

Spot gaps in your systems before production does , missing idempotency, unbounded queues, cache stampedes, hot shards, and drive the fixes

Debug the incidents that cross layers, where the answer isn't in one service's logs but in how the pieces interact

Write design docs and RCAs that make the platform's behavior legible to 80+ engineers building on it

The terrain:

Runtime : Node.js (TypeScript), Go, on GKE

Messaging & async : GCP Pub/Sub, Cloud Tasks, Redis

Storage : MongoDB, Firestore, ClickHouse, ElasticSearch

Observability : metrics, tracing, and alerting you'll help make sharper

Requirements:: 4+ years of backend engineering experience, with deep hands-on work in Node.js and/or Go

Strong systems understanding across the stack , application, database, and infrastructure, proven on high-throughput production systems

Deep experience with queueing and async processing , Pub/Sub, Kafka, RabbitMQ, Cloud Tasks, or similar, delivery semantics, ordering, backpressure, idempotency

Database depth beyond CRUD , indexing strategies, sharding, replication, query performance, and safe migrations on SQL or NoSQL stores at scale

Hands-on with Redis or other in-memory stores , caching patterns, data structures, and their failure modes

Production experience with cloud infrastructure , Kubernetes, autoscaling, resource limits, and how they behave under pressure (GCP preferred)

You write clear design docs and rigorous test cases as a habit, not on request

Champion of AI-assisted engineering , you make agents produce accurate, production-quality code, fast

On AI: We're past the debate. AI is part of how we build, and we expect you to be better at it than most .

That means making agents do real work: producing code that's accurate, tested, and slop-free , fast. On platform code, the bar is higher, not lower: a sloppy merge here doesn't break a feature, it breaks the floor everything stands on. You review AI output with that in mind, and you ship at a pace engineers without this skill can't match.

What you're built for: You understand systems in depth , not just the API surface, but what happens under load, at the tail, when the network partitions

You've taken real production hits , a migration gone sideways, a queue that wouldn't drain, a database that fell over, and each one made your designs better

You're the engineer teammates pull in when the bug crosses layers , app, database, infra, because you can hold all three in your head

You write design docs people actually read , clear trade-offs, honest risks, a real recommendation

You treat capacity and failure modes as design inputs , not afterthoughts

When production breaks, your first instinct is curiosity, not panic

Bonus Points: You've operated systems at comparable scale , billions of events, thousands of instances

Experience with ClickHouse, ElasticSearch, or Firestore in production

You've done platform work before, the unglamorous layers everyone depends on and nobody notices until they fail

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