---
title: 'Applied AI Engineer at Monte Carlo'
canonical: 'https://feeny.ai/job/applied-ai-engineer-monte-carlo-americas-385312hg9b1q'
type: 'job'
last_seen: '2026-09-08'
---

# Applied AI Engineer at Monte Carlo

- **Company:** Monte Carlo
- **Location:** Americas
- **Compensation:** $180k–$240k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-08-27
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/montecarlodata/bee6315c-3bc5-49b8-a6c0-1c36b3b59de6

## Job description

## 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 montecarlodata.com http://montecarlodata.com.

## 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. 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 pointed 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.
- 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

## WHY MONTE CARLO

- We created the data observability category and we're doing it again with agent observability https://www.montecarlodata.com/blog-what-is-ai-agent-observability/ — 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.

## #LI-REMOTE

## #BI-REMOTE

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 https://www.montecarlodata.com/blog-2025-databricks-data-governance-partner-of-the-year/?utm_source=chatgpt.com

We were recently recognized as the #1 Data Observability Platform by G2 for the 4th consecutive quarter. See our G2 reviews here! https://www.g2.com/reports/grid-report-for-data-observability-spring-2025.embed?featured=monte-carlo&secure%5Bgated_consumer%5D=7d02ec0a-326a-40fa-8a44-fab49f67c5f1&secure%5Btoken%5D=6b3c29d18ea50ae0005295b5c63994f97c01cae81bbd3f9ea6abff73c40fde51&utm_campaign=gate-2063400

Monte Carlo Named to G2's Best Software Products of 2026 https://www.montecarlodata.com/blog-monte-carlo-g2-best-software-product-of-2026/

Monte Carlo was featured on Database Trends and Applications (DBTA’s) Trend-Setting Products for 2025! https://www.dbta.com/Editorial/Trends-and-Applications/Trend-Setting-Products-in-Data-and-Information-Management-for-2025-167115.aspx

We are super proud to be named the 2026 Best Place to Work by Built In! https://builtin.com/awards/us/2026/best-places-to-work

Beware of Imposter Recruiters and Job Scams

- All official communication from our recruiting team will come from an @montecarlodata.com http://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.

## About Monte Carlo

## Company Overview
- **One-liner**: Monte Carlo provides an autonomous observability platform that unifies data and agent observability to monitor, troubleshoot, and improve production AI systems.
- **Entity Type**: Private (Series D, $135M raised May 2022)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2019
- **Founders**: Not publicly listed as founders in provided sources (likely Lior Gavish as CTO and others; not explicitly named)

## Core Business
- **Primary industries**: Data infrastructure, AI observability, Software Development
- **Target customers**: Enterprise B2B – data and AI teams at large organizations (400+ enterprise customers including JetBlue, Roche)
- **Mission / purpose**: "Reduce data downtime" and enable enterprises to ship trusted AI at scale by providing reliability infrastructure for agents and data pipelines.

## Products & Services
- **Monte Carlo Platform**: An end-to-end autonomous observability platform that monitors data pipelines, agents, and AI systems in production. Includes features like anomaly detection, data lineage, and incident resolution. Type: SaaS.
- **Agent Trust Platform**: A newer capability focused on monitoring and troubleshooting AI agents in production, unifying data observability with agent observability.

## Market Standing
- **Valuation / Market Cap**: Not disclosed (private); total funding $236M (Seed, Series A $16M, Series B $25M, Series C $60M, Series D $135M)
- **Key Metric**: Total Funding $236M; Annual Revenue reported as $2M (likely placeholder/outdated – LinkedIn often shows symbolic revenue). 400+ enterprise customers, 10M tables monitored, 1,000 incidents resolved daily.
- **Notable Investors/Partners**: Accel (lead in Seed & Series A), Redpoint, Notable Capital (Series B), ICONIQ Growth (Series C), IVP (Series D). Partners include Alation, Sigma, Fivetran, Snowflake, Databricks.
- **Growth Signals**: +9.8% YoY headcount growth (212 employees), operates in 14 countries, recent launch of "Trusted Data for AI (TDAI) Advisory Council" (Aug 2024), support for Apache Kafka and vector databases (Nov 2023). Recognized by Forrester with a TEI study showing 375% ROI and $1.5M avoided losses.

## Competitive Advantages
- First-to-market autonomous observability platform combining data and agent monitoring.
- Deep integrations with modern data stacks (Snowflake, Databricks, dbt, Fivetran, Kafka) and AI frameworks.
- Strong enterprise adoption (JetBlue, Roche) and validated ROI (Forrester study).
- 80% reduction in data downtime reported by customers.

## Strategic Focus
- Expanding from data observability to "agent trust" to support the explosion of AI agents in production.
- Building out the "Monte Carlo for AI" go-to-market, with a dedicated Advisory Council.
- Scaling global presence (14 countries) and deepening partnerships with major cloud and data platforms.

## Why Work Here
- **Culture**: Values include "Measure in minutes" (urgency), "Ship and iterate" (high expectations with iteration), "Customer impact", and "Beat the odds" (ambition, learning from failure). Emphasizes a positive, team-oriented environment.
- **Work policy**: Not explicitly stated as fully remote, but based on LinkedIn locations (HQ in San Francisco, offices in multiple US cities, and distributed teams across 14 countries) it likely supports hybrid/remote. The careers page mentions "Global Office and Culture Manager".
- **Engineering culture**: High degree of execution and collaboration; tech stack includes Python, TypeScript, React, GraphQL, Snowflake, Databricks, and modern observability tools (DataDog, PagerDuty). Employees come from top tech companies (Google, Snowflake, Twilio, Confluent).
- **Perks**: Not detailed in public sources, but the "Have fun" value and focus on authenticity suggest a strong culture. Recent departures of some senior sales and marketing leaders may indicate restructuring.
- **Candidate note**: Official communication only from @montecarlo.ai emails; beware of scams.

## Sources
1. [montecarlo.ai - About Us](https://montecarlo.ai/about-us)
2. [montecarlo.ai - Homepage](https://montecarlo.ai/)
3. [Monte Carlo Careers](https://www.montecarlodata.com/careers-at-monte-carlo/)
4. [LinkedIn Company Profile](https://www.linkedin.com/company/monte-carlo-data)

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