Continuously improve AI agents with agent observability, evaluation, tracing, and experimentation. Backed by Battery.
About the role
Our Backend Engineering team builds all of the highly scalable distributed services that power Arize’s ML observability platform. While Go is our primary language for these distributed systems, the team also maintains services and tools written in Python, Java, and TypeScript.
What they're looking for
- 5+ years of experience working with high-performance backend systems
- Strong experience writing Go, Python, TypeScript/Node, Java, or similar server programming languages
- Enthusiasm and interest in the AI and LLM ecosystem, with a desire to learn and stay updated on emerging technologies
- Previous work building and operating highly complex SaaS platforms/systems
- Knowledge of working with public clouds & container orchestration - AWS, GCP, Azure, Kubernetes, etc
More about this role
AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimize their AI systems. That’s where we come in. Arize AI is the leading AI & Agent Engineering observability and evaluation platform , empowering AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, Arize AX unifies build, test, and run in a single workspace—so teams can ship faster with confidence.
We’re a Series C company backed by top-tier investors, with over $135M in funding and a rapidly growing customer base of 150+ leading enterprises and Fortune 500 companies. Customers like Booking.com , Uber, Siemens, and PepsiCo leverage Arize to deliver AI that works.
Our Backend Engineering team builds all of the highly scalable distributed services that power Arize’s ML observability platform. While Go is our primary language for these distributed systems, the team also maintains services and tools written in Python, Java, and TypeScript. The expectation and scope of every individual on this team is high, whether it’s finding the most efficient way to compute model evaluation metrics across...
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