About the role
About the Role
We’re hiring an AI Infrastructure Engineer to shape and scale the backend systems that power our AI platform. As a Series A company, your work will be foundational, enabling safe, efficient, and reliable AI workflows from end to end.
What You’ll Do
-
Design and implement scalable backend architectures for AI workloads (inference, orchestration, monitoring).
-
Own distributed job orchestration with Temporal and related systems.
-
Improve data pipeline performance by designing smarter caching strategies (e.g., file deduplication, hot/cold storage, Redis caching layers) to reduce redundant compute and API calls.
-
Build observability, monitoring, retries, and fault tolerance into all workflows.
-
Manage infrastructure reliability, incident response, and performance.
-
Develop tooling and platform infrastructure to support rapid growth.
-
Partner with ML engineers to bring models to production at scale.
What We’re Looking For
-
4+ years of backend engineering (Python is a must).
-
Strong background in distributed systems, job orchestration, and task queues.
-
Deep knowledge of concurrency, parallelism, and multithreading—including async/await, event loops, thread pools, synchronization primitives, deadlocks, and race conditions—is a must. You should know how to design systems that maximize throughput without sacrificing correctness or safety.
-
Hands-on experience with Temporal, Redis, Airflow, Celery, RabbitMQ (or similar).
