Senior SDE - Platform

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

What are we hiring for?

The platform underneath our two biggest systems.

Workflows executes 21.5 billion automation actions a month from 3.1 billion enrollments. Conversations moves 2.6 billion messages in the same window. Together they run on thousands of pods, across 50+ kinds of deployments, on top of multiple database engines, GCP Pub/Sub, Cloud Tasks, and Redis, with traffic peaking above 28,000 requests per second and growing double-digit percent every month.

Product engineers build the features. This role owns what the features stand on, the queues, the storage layers, the caching, the infrastructure, and makes sure it holds when the growth curve does what our growth curve does.

We're not looking for someone who knows one layer. We're looking for the engineer who understands the whole vertical: how a query plan behaves at 1.5 TB of indexes, why a pod eviction turned into a message backlog, what a Redis failover actually does to the request path. Databases, infra, cloud, you see through all of it.

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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