What you'll do- Build and iterate on the core AI agents that power Sazabi
- Design systems for anomaly detection, root cause analysis, and automated debugging
- Work on prompt engineering, tool use, and agent orchestration
- Improve reliability, latency, and correctness of AI-driven workflows
- Experiment rapidly with new models, frameworks, and techniques
- Translate messy real-world production issues into structured AI workflows
What we're looking for- Strong experience working with LLMs in production (agents, RAG, tool use, etc.)
- Deep curiosity about how and why AI systems fail-and how to fix them
- Ability to prototype quickly and iterate based on real-world feedback
- Strong engineering fundamentals (this is not just prompt hacking)
- Comfort operating in a fast-moving, experimental environment
- Bonus: experience with observability, debugging systems, or developer tools
Our techOur stack is TypeScript end-to-end. We use PostgreSQL for relational data, Temporal for durable execution, and the Vercel AI SDK to orchestrate our AI agents against the latest models. At the infrastructure layer, we use Terraform, Kubernetes, and AWS.
Some of the most interesting engineering problems we grapple with are:
- AI agents at production scale
- Log processing at scale
- Multi-region data architecture
- Real-time streaming infrastructure
- Evals and reinforcement learning
- Zero-downtime deployments and fast rollbacks
What we offer- Competitive salary and equity
- Free lunches (in-office only)
- Health, dental, and vision insurance
- Unlimited paid time off
- Paid parental leave
