About Monte Carlo

Monte Carlo is the agent trust platform that unifies data and agent observability to monitor, troubleshoot, and improve production AI systems. As enterprises prepare to deploy thousands of agents across business-critical use cases, Monte Carlo provides the reliability infrastructure to support them along this AI transformation, from human-guided agents to fully autonomous operations. Founded in 2019 and backed by leading investors, Monte Carlo empowers data and AI teams to ship trusted AI at scale. Learn more at montecarlo.ai

The Role

We're hiring a Senior Backend Engineer to design and build the core of our agent trust platform: the architecture, distributed systems, and backend services that let enterprises trust AI in production. You'll get a problem statement, not a spec, and own it from system design to a tested, deployed product. React experience is a plus for the times a feature needs a front end.

What You'll Do

• Own the architecture and system design for core platform services: make the calls, write down the tradeoffs, and evolve the design as scale grows.
• Build and run production-grade backend services and APIs in Python or Java that power Monte Carlo's core platform and agentic systems.
• Design and scale distributed systems that stay reliable, observable, and fast as customer data and agent volume grow.
• Take vague problem statements to production: prototype, architect, build, test, deploy, and own it after launch. Start simple and evolve it without over-building up front.
• Build and maintain data pipelines behind analytics, ML, and customer-facing features.
• Work with product, ML, and infrastructure partners to ship customer value, and pick up React work when a feature needs it.

What We're Looking For

• Backend depth. 5+ years shipping production backend services in Python or Java, with real experience designing, running, and debugging APIs and services under load.
• Architecture and system design. You've designed systems from a blank page, weighed the options, and can defend the tradeoffs: consistency, availability, latency, and cost.
• Distributed systems. You've built and scaled distributed architectures yourself and run them in production: services, queues, event-driven flows, data stores, and what happens when they fail.