By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, High Level 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, High Level operates as a general, remote-first organization built for speed and ownership. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.

Our impact Every month, High Level enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Learn more about us on our You Tube Channel or Blog Posts

Four databases. Each service has owners, each feature has engineers, each database has experts. What we don't have is the person who holds the entire distributed system in their head, who sees that the retry policy in one service and the queue configuration two hops away are, together, a cascading failure waiting for Black Friday.

You start with Workflows, High Level's automation engine, and one of the largest systems in the company: 3.1 billion enrollments and 21.5 billion action executions every month, traffic peaking at 28,000+ requests per second, running across thousands of pods on GCP with Pub/Sub, Cloud Tasks, Redis, and multiple database engines underneath.

From there, your blast radius grows, into Conversations (2.6 billion messages a month) and a brand-new Ticketing system being built right now, where you get to make sure it's born right instead of fixed later.

Own the architecture health of a billion-scale distributed system , its failure modes, capacity limits,



consistency guarantees, and the interactions between 50+ deployments that no single team can see
Hunt gaps proactively , single points of failure, unbounded queues, missing idempotency, thundering herds, quiet data-loss windows, and drive the fixes before they become incidents

Set the standard for how AI-assisted engineering works safely on systems this critical, where a bad merge doesn't cost a demo, it costs real businesses their revenue

Runtime: Node.js (Type Script), Go, thousands of pods on GKE

GCP Pub/Sub, Cloud Tasks, Redis

MongoDB, Firestore, Click House, Elastic Search

10+ years of engineering experience with deep, hands-on ownership of large-scale distributed systems , comparable scale strongly preferred: hundreds of services or thousands of instances, billions of daily events
~ Strong with multiple storage engines (SQL and NoSQL), you reason about consistency models, indexing at scale, and when each engine is the wrong choice
~ Expert-level depth in Redis or comparable in-memory systems , including their failure modes under memory pressure and network partition
~ Production experience on Kubernetes at scale , resource limits, autoscaling behavior, what actually happens when a node pool dies

~ Fluent in Node.js and/or Go , enough to prototype your own proposals and ship fixes on the critical path

The critical paths of Workflows have named owners, capacity models, and tested failure modes , because you made it so
Pub/Sub, Cloud Tasks, GKE, Firestore
You've done this job before under another name, "the systems person," principal engineer, architect-who-still-codes

LI-Remote #LI-HB1

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📌 Systems Engineering Staff (Monterrey)
🏢 HighLevel
📍 Monterrey