About the Role:

We're looking for a backend engineer who can own and evolve our Ticketing & Ride Request (TRR) system — the service that handles everything from a rider tapping "Request a Ride" to a vehicle arriving at their station. This is a high-throughput, event-driven system where reliability isn't optional; real people are standing on a platform waiting for their ride.

You'll also contribute to the broader backend infrastructure that supports our suite of applications — think message queues, caching layers, database performance, and service-to-service communication. If you've ever been the person on your team who gets pulled into debugging the weird one, you'll feel at home here.
We care far more about how you think than where you went to school or how many years are on your resume. To show us you actually read this posting and aren't auto-applying, start the first sentence of your cover letter with the words "Ride request received." — we'll know you're paying attention.

• Designing and maintaining the TRR backend — ride matching, station queueing, request lifecycle management, and fare calculation
• Building and operating services on top of PostgreSQL, Redis, and RabbitMQ in a Kubernetes environment
• Improving system observability — tracing, metrics, alerting — so we catch problems before riders do
• Collaborating on API design with our frontend, mobile, and operations teams
• Participating in incident response and post-mortems; we run blameless retros and expect engineers to dig into root causes, not just restart pods
• Contributing to capacity planning and load testing as we scale to new deployment sites

• Deep fluency in at least one backend language (TypeScript/Node.js, Go, Python, or Java) — we use TypeScript heavily but value polyglot thinkers
• Hands-on experience with PostgreSQL (query optimization, indexing strategies, schema migrations) and Redis (caching patterns, pub/sub, rate limiting)
• Familiarity with message broker patterns — RabbitMQ preferred, but Kafka or NATS experience translates well
• Solid understanding of distributed systems fundamentals: consistency models, failure modes, backpressure, idempotency
• Experience with Kubernetes in production — not just deploying, but debugging networking issues, resource contention, and pod lifecycle surprises
• Strong debugging instincts: you're the kind of engineer who reaches for tcpdump or strace before restarting the service

• Experience with event-driven architectures or CQRS patterns
• Familiarity with Temporal.io or similar workflow orchestration frameworks
• Background in transit, logistics, or any domain where latency directly impacts a physical-world experience
• Experience operating multi-tenant or multi-site deployments
• You've written a runbook that actually saved someone at 2am