
Senior Backend Engineer
Required skills
- backend services
- APIs
- distributed systems
- Python
- Java
- system design
- architecture
- tradeoffs
- scale
- reliability
Nice to have
- React
- agentic systems
- LLM
- AWS
- PySpark
Role summary
Monte Carlo is seeking a Senior Backend Engineer to design and build the core of their agent trust platform, focusing on architecture, distributed systems, and backend services for production AI systems. The role involves taking ambiguous problem statements from system design to tested, deployed products, with ownership of production services, APIs, and data pipelines. Experience with Python or Java, distributed systems, system design, and production operations is required. Familiarity with React, agentic systems, AWS, and PySpark is a plus. This is a remote-first, full-time position.
About Monte Carlo
Monte Carlo is the agent trust platform that autonomously observes, fixes, and continuously improves agents and the data that powers them — so enterprises can scale trust at the speed of AI. Bayes, our AI teammate, does the work: detecting, triaging, troubleshooting, and fixing issues in AI agents and the data powering them. The result is self-improving agents that get better with every fix instead of drifting. More than 400 enterprises, including Amazon, PepsiCo, CNN, Nasdaq, and JetBlue, rely on Monte Carlo. 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.
0-to-1 ownership. You've taken ambiguous problems to a deployed product, owned testing and deploy, and kept owning it after launch. You move with urgency and treat outcomes as yours.
Nice to have. React for the times a feature needs a front end. Agentic or LLM-powered systems in production are a strong plus, as are data pipelines on AWS and PySpark.
This Is Not For You If
You want a detailed spec before you start building.
Your backend experience is mostly CRUD apps on a single service, or you've only built inside architectures someone else designed.
You'd rather hand off testing, deployment, and on-call than own them.
You're mainly a frontend engineer looking to grow into backend.
#LI-REMOTE
#BI-REMOTE
Why Monte Carlo
We created the data observability category, and we're doing it again with agent trust — you'll build where the market is forming, not where it's settled
Series D, $236M raised, backed by Accel, Redpoint, Notable Capital, ICONIQ Growth, and Salesforce Ventures
Customers include HubSpot, Fox, Nasdaq, Toast, and Mercado Libre — your work ships to enterprises with real stakes
Snowflake Partner of the Year and a verified connector in Anthropic's Claude AI directory
Remote-first by design since day one, and recognized as a Best Workplace for it
Competitive compensation, equity, and a remote-first environment.
Come As You Are
Equality is a core tenet of Monte Carlo's culture. We are committed to building an inclusive global team that represents a variety of backgrounds, perspectives, beliefs, and experiences.
Monte Carlo is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We are proud to be recognized for our world-class employee experience:
Monte Carlo Named 2025 Databricks Data Governance Partner of the Year
We were recently recognized as the #1 Data Observability Platform by G2 for the 4th consecutive quarter. See our G2 reviews here!
Monte Carlo Named to G2's Best Software Products of 2026
Monte Carlo was featured on Database Trends and Applications (DBTA’s) Trend-Setting Products for 2025!
We are super proud to be named the 2026 Best Place to Work by Built In!
Beware of Imposter Recruiters and Job Scams
All official communication from our recruiting team will come from an @montecarlodata.com email address.
We will never ask candidates to provide sensitive personal information (such as bank details, social security numbers, or payment) at any stage of the recruitment process.
We will never request payment for equipment, training, or application processing.
Our open positions are always listed on our official careers page: https://jobs.ashbyhq.com/montecarlodata.
If you are contacted by someone claiming to represent Monte Carlo but you’re unsure of their legitimacy, please reach out to us directly at recruiting@montecarlodata.com before sharing any personal information.
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