Senior Go Engineer - AI Platform Backend

Yo is building something new in AI: a platform that turns everyday smartphones into a global AI network. We are an established mobile business moving into AI infrastructure, with real users, real revenue, and an ambitious roadmap. You will join at the ground floor and build the backend that powers the network.

This is a foundational hire. You will shape the architecture from day one and see your work run at scale.

Key Responsibilities

• Design and build the orchestration backend in Go: task routing, device registry, result collection
• Own the real-time layer - long-lived device connections, heartbeats, streaming task results, reconnect handling
• Design and version the public REST API used by clients and SDKs
• Own the PostgreSQL schema - migrations, integrity, safe concurrent updates under load
• Maintain and extend the Django admin panel used for operations and configuration
• Make the system observable - structured logs, metrics, and traces that let you diagnose unstable network and protocol errors in production
• Test the hard parts: streaming, timeouts, concurrency, disconnects, protocol edge cases

Required Qualifications

• 5+ years backend experience, with strong production Go: context, net/http, goroutines, channels, timeouts, cancellation, careful error handling, generics
• Real-time protocols in production: WebSocket, SSE, response streaming, heartbeat/ping-pong, long-lived connections, disconnect and retry scenarios
• Backend API design: REST, OpenAPI, request validation, correct use of headers, JWT authentication
• PostgreSQL and SQL: transactions, indexes, migrations, data integrity, concurrent updates
• Reliability and operations mindset: observability, structured logging, diagnosing flaky network and protocol failures
• Testing discipline: unit and integration tests that cover streaming, timeouts, concurrent scenarios, and protocol edge cases
• Working Python: able to maintain and extend a Django admin without hand-holding

Nice to Have

• Understanding of AI/LLM infrastructure: what a model is, how inference works, constraints of running models on end-user devices
• Experience with fleets of mobile or IoT clients on unreliable networks
• gRPC and Protocol Buffers
• Docker and container-based deployment; CI/CD ownership
• Message queues or task-scheduling systems
• Experience designing systems where money or user earnings depend on correct accounting

Location & Logistics

• Remote / flexible (team in EU time zones)
• Reports to the CTO

Originally posted on Himalayas