AI Infrastructure EngineerStack AI, Inc. • San Francisco, CA, United States
AI Infrastructure Engineer
Stack AI, Inc. • San Francisco, CA, United States
8 hours ago
Job type
- Full-time
Job description4+ 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). Experience with LLM serving and routing fundamentals (rate limiting, streaming, load balancing, budgets). Comfortable with containers & orchestration : Docker, Kubernetes. Familiarity with cloud platforms (AWS / GCP) and IaC (Terraform). Experience with multiple storage systems : S3, Postgres, MongoDB, Redis, and Elasticsearch. Track record scaling systems in startups or fast‑paced environments. Understanding of deploying, monitoring, and optimizing AI / ML systems in production with strong CI / CD practices. Play a foundational role at a fast‑growing Series A startup that is shaping the future of AI in enterprise workflows. Collaborate across Product, ML, and Platform teams, being the bridge between AI logic and scalable execution. Build infrastructure that enables real value for large enterprises : low‑code, secure, and scalable AI workflows. Join a company that’s scaling thoughtfully and values developer experience.
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
Why You’ll Love Working Here
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