Key skills
AirflowDistributed SystemsPostgresPythonFastAPI
About this role
Role Overview
- Own the execution engine. The runtime, scheduling, and sub-agent parallelization that run every agent on the platform.
- Make long-running work durable. Build checkpointing, resumption, and recovery so agents survive failures and restarts and pick up exactly where they left off.
- Shape the execution model. Decide how work is scheduled, queued, and moved from synchronous to asynchronous, so the platform stays correct and responsive as load grows.
- Engineer for scale and reliability. Hold the engine to strict health targets for worker freshness, deploy safety, and drain time, and keep latency and throughput strong as volume grows.
- Keep the engine open to the ecosystem. Make it straightforward to bring new agent harnesses, orchestration frameworks, and model capabilities into the runtime.
Requirements
- 5+ years building backend systems in production, with real depth in distributed systems.
- Hands-on experience with durable execution or workflow orchestration (Temporal, Cadence, Airflow, or equivalent), with a way of thinking rooted in idempotency, state machines, and failure recovery.
- Strong command of concurrency, queueing, retries, and fault tolerance under load.
- Strong in Python and modern backend frameworks (FastAPI or similar), with sound database fundamentals (Postgres or similar).
- You're drawn to the correctness problems that everything else quietly depends on.
Tech Stack
- Airflow
- Distributed Systems
- Postgres
- Python
Benefits
- Offers Equity