Asks for

The ad says“Work on prompt engineering, tool use, and agent orchestration”
The ad says“Our 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.”
The ad says“Our 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.”
The ad says“Strong experience working with LLMs in production (agents, RAG, tool use, etc.)”
The ad says“Our 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.”
The ad says“Work on prompt engineering, tool use, and agent orchestration”
The ad says“Strong experience working with LLMs in production (agents, RAG, tool use, etc.)”
The ad says“Our 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.”
The ad says“Our 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.”
The ad says“Work on prompt engineering, tool use, and agent orchestration”
The ad says“Our 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.”
The ad says“Our 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.”

What we're building

In 2026, we're on the verge of an "infinite software crisis." How will we support, maintain, and operate the explosion in application development?

Our answer is Sazabi: the AI-native observability platform for fast-moving engineering teams.

Sazabi gives teams a single place to ask questions about their production systems in plain language, automatically visualize what's happening, and get to the root cause 10x faster. No tedious instrumentation. No dashboard configuration. No alert tuning. Just answers.

We're backed by tastemakers from the world's top AI companies: Vercel, Graphite, Daytona, Browserbase, LangChain, Mastra, Replit, and more.

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 tech

Our 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

Learn how we think and work

Our team comes from Brex, Y Combinator, Rootly, Google, Doppel, and other top tech companies. We move fast and hold a high bar for both the product and each other.

We operate according to six values:

Integrity. We say what we mean, do what we say, and own our mistakes.
Urgency. We treat every week like it matters, because it does.
Craftsmanship. We care deeply about the quality of our work.
Service. We're here to make developers’ and our teammates’ lives easier.
Kindness. We're direct, and we move fast, but we treat each other well.
Fun. We genuinely enjoy working together. If you don't love what you're building and who you’re building it with, what's the point?

You can find out more about us here:

Watch our launch video
Read the manifesto
Follow us on X and LinkedIn

The Sazabi philosophy

Sazabi is more than just a tool. It’s also a philosophy. We're taking a radically different approach to observability, centered on three big ideas:

Less is more. Most observability platforms drown you in dashboards and modules that you don’t need. We believe observability needs less UI, not more. Sazabi surfaces exactly the information you need in a simple, beautiful chat interface.
Logs are all you need. The “three pillars of observability” idea is outdated. Sazabi accepts only one kind of telemetry: logs. This simplifies the instrumentation and product experience dramatically.
Monitoring is dead. Creating and maintaining static alerts for a fast-evolving system is a fool’s errand. The future is agentic anomaly detection: AI agents that automatically check your app for issues 24-7.

We came to these beliefs the hard way — after years of building infrastructure and responding to incidents with tools that offered plenty of bells and whistles, but no clear answers when it counted most.

If this way of thinking resonates with you, come build with us.