The job, in the employer's words
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 building the products that tell enterprises whether their AI agents can be trusted — and we need someone who works end to end, from an ambiguous problem statement through research, prototyping, and production. You'd get the problem, not the spec: research the approaches, prototype, prove what works, build it, and integrate it into the platform alongside our engineering and data science teams. This role exists because agent observability moved from roadmap to revenue faster than anyone predicted, and the work is now on the critical path.
What You'll Do
Take an open problem end-to-end — from research and prototyping through production, killing what doesn't work before it becomes someone's roadmap
Design and ship agent-powered features — root-cause analysis, incident triage, monitor generation — and integrate them into the platform with our engineering team
Build the eval infrastructure that makes those features safe to change: golden datasets, regression suites, offline and online scoring, and the judgment calls about what "good" means
Own retrieval and context pipelines over customer metadata, lineage, and query history, and instrument agent behavior in production — traces, failure taxonomies, cost and latency budgets — to close the loop on quality
Partner with data science on detection quality and experiment design, and with PM on what an agent should do versus what it merely can do
Set the technical bar for how we build with LLMs — patterns, guardrails, and the internal tooling other engineers reuse
What We're Looking For
You've built agents in production with real users. Not integrated a framework. Not worked on a team that had one. Built them — agents with real autonomy and internal loops, where the model uses tools and decides what to do next without a human in the middle, and you kept them running once real users showed up. RAG with a wrapper doesn't count. Neither does a set of MCP tools point at an API.
You've run evals and monitored agents after launch. Agents are non-deterministic, so normal tests don't work on them. You've owned an eval framework — golden datasets, regression suites, offline and online scoring — not a folder of one-off scripts. And you've watched agents in production, not just in dev.
Python, plus an ML or data science background. Python is your daily language and you're solid on the backend, though you don't need to be a distributed systems specialist. You understand models well enough to reason about how they behave — you're not an application engineer calling someone else's API.
3+ years in ML, data science, or software engineering — at companies that ship quickly. Startups, AI-native teams, or high-growth product companies where the release cadence is measured in weeks, not quarters. If all of your experience sits inside large, process-heavy organizations with the platform already built for you, this seat will be a hard adjustment.
You work from a problem, not a spec. Handed an ambiguous problem statement, you design the experiment, build the smallest version to test it, and take what works into production.
You use AI tools every day. Claude or its equivalents are part of how you write code and do research, not something you tried once. This is backend and model layer work, by the way — no frontend.
You'd rather ship than polish. Most of this work needs a good answer quickly, not a perfect one eventually. You can tell which problems are the exception and deserve real depth — and you'll say no to the version that demos well and falls apart in production.
Nice to have: statistics and hypothesis testing, applied rather than theoretical. Building and maintaining MCP servers. Experience in the data and cloud space — Snowflake, Databricks, dbt, Airflow.
This Is Not For You If
Your AI work is retrieval with a wrapper, or MCP tools pointed at an API — nothing that decides and acts on its own
Your LLM experience is prototypes, notebooks, and demos that never carried production traffic
You need a fully specified problem before you start, or you're uncomfortable with the ambiguity of a category being invented in real time
#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
Monte Carlo Named to G2's Best Software Products of 2026
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.
Requirements mentioned
- You've built agents in production with real users. Not integrated a framework. Not worked on a team that had one. Built them — agents with real autonomy and internal loops, where the model uses tools and decides what to do next without a human in the middle, and you kept them running once real users showed up. RAG with a wrapper doesn't count. Neither does a set of MCP tools point at an API.
- You've run evals and monitored agents after launch. Agents are non-deterministic, so normal tests don't work on them. You've owned an eval framework — golden datasets, regression suites, offline and online scoring — not a folder of one-off scripts. And you've watched agents in production, not just in dev.
- Python, plus an ML or data science background. Python is your daily language and you're solid on the backend, though you don't need to be a distributed systems specialist. You understand models well enough to reason about how they behave — you're not an application engineer calling someone else's API.
- 3+ years in ML, data science, or software engineering — at companies that ship quickly. Startups, AI-native teams, or high-growth product companies where the release cadence is measured in weeks, not quarters. If all of your experience sits inside large, process-heavy organizations with the platform already built for you, this seat will be a hard adjustment.
- You work from a problem, not a spec. Handed an ambiguous problem statement, you design the experiment, build the smallest version to test it, and take what works into production.
- You use AI tools every day. Claude or its equivalents are part of how you write code and do research, not something you tried once. This is backend and model layer work, by the way — no frontend.
- You'd rather ship than polish. Most of this work needs a good answer quickly, not a perfect one eventually. You can tell which problems are the exception and deserve real depth — and you'll say no to the version that demos well and falls apart in production.
- Nice to have: statistics and hypothesis testing, applied rather than theoretical. Building and maintaining MCP servers. Experience in the data and cloud space — Snowflake, Databricks, dbt, Airflow.
- 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.
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