Senior Software Engineer – Platform

What are we hiring for?

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