Monte Carlo
montecarlo.ai
Actively hiring
Data Observability
Data Quality & Observability
View 3 jobs
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Overview
News
Technologies
Salaries
Products
People
Growth
Offices
Jobs
Financials
Funding and backers
Total raised
$151M
2 rounds on record
Latest round
$135M
Series D · May 2022 · 4 yr ago
Team
501-1000
employees, estimated range
Hiring now
10
+7 posted in 30 days
Backers
4
Accel, IVP
The money's age
Long since its last round
52 months since the last round · and hiring
Momentum
43/100
hiring for its size, in the news
Last sign of life
this month
a posting or a news item
The story so fareach stage dated by its source: the company's releases, the press, registers, filings
May 24, 2022
Raised a round Series D $135M · Monte Carlo
Sep 16, 2020
Raised a round Venture round $16M
Tractionpublic signals, read monthly
Web presence
top 6%
of 133.2M domains by links, Common Crawl
Funding rounds
| Round | Date | Amount | Investors | Source |
|---|---|---|---|---|
| Series D | May 2022 | $135M | — | Monte Carlo |
| Venture round | Sep 2020 | $16M | — | montecarlodata.com |
Registers and programsofficial lists and records that name the company
| Legal entity | Monte Carlo AI, Inc. | its site |
Backed byfrom the funds' portfolios and accelerator catalogs
Accel 
IVP 
Notable Capital 
Redpoint Ventures Investors in similar startupsfunds that back the companies most like this one
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Overview
Monte Carlo is a San Francisco company founded in 2019 that created the data observability category. Its platform learns the expected behaviour of tables and pipelines, detects freshness, volume and schema anomalies and traces the lineage of a broken dashboard back to its cause. It has extended monitoring to the vector stores and unstructured data feeding generative AI systems.
News
The Open vs. Closed AI Debate Misses the Point: Most Orgs Can't Measure Either
The discussion around enterprise AI often focuses on the choice between open-weight models, like Meta's Llama and models from Mistral, vs. closed frontier models, such as Anthropic's Claude and OpenAI's GPT models. While closed frontier models promise sta
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Report
LLM Evals: What They Are and How to Get Started
TL;DR: LLM evals are a structured way to grade an AI system's outputs against a standard of quality, the discipline that turns the assumption the AI is working into proof. Every eval has three components: a test case, a response, and a grader (code-based,
Read more
Report
Prompt Versioning: Why Your AI Prompts Need the Same Rigor as Your Code
If your team is building anything powered by LLMs, there's a good chance your prompts have already changed a dozen times since launch. Maybe someone tweaked a system message to fix a tone problem, or maybe an engineer added a new instruction to stop the m
Read more
Report
How We Measure What AI Says About Us: An LLM Visibility Audit
We built a category once and could see ourselves winning. Building the next one, we couldn't see the scoreboard. The first category we built, we could see ourselves winning. Data observability didn't exist as a market until we helped make it one, and the
Read more
Report
How AI Anomaly Detection Catches the Problems Your Tests Miss
Your test suite is a record of every failure you've faced. You wrote a test because something broke once, and you never wanted it to happen again. Which is great, except it means your tests are always backward-looking. The failures that actually keep you
Read more
Report
Production is Not a Bigger Dev: How Evals Leave Your Agents Unwatched
Every AI team that has shipped an agent has likely utilized a golden dataset evaluation; essentially a curated set of prompts with expected completions, a scoring harness, a threshold to clear before merge. Evals are very useful, as they let us take a hig
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Report
Root Cause is Half the Job; Remediation Agent Ensures Every Analysis Ends in a Plan
When a monitor fires on a data table or agent, Monte Carlo streamlines the investigation process for you via our Troubleshooting Agent. It gives you a root cause, the evidence behind it, and a verification checklist. The last step in the resolution proces
Read more
Report
Tired of Alert Fatigue? Triage Agent Helps You Manage Your Alerts
Most data and AI teams have one channel where all notifications on the health of their system get sent to. Whether it's an alert about a freshness break on a critical revenue table, a volume dip on a staging table that nobody really queries, or a monitor
Read more
Report
Why AI Agents Go Rogue: Common Failure Patterns and How to Remedy Them
When most people picture an AI agent failing, they picture a red alert, a 500 server error, or a massive stack trace. In reality, production failures almost never look like that. Across the deployments we monitor, the incidents that truly destroy user tru
Read more
Report
The EU AI Act: Are You Prepared for What's Next?
Given the plethora of LinkedIn posts on the subject, if you deploy a general purpose agent or other AI system to the EU market, you are likely aware that the majority of EU AI Act Article 50 transparency rules went into effect on 2 August 2026. At a high
Read more
Report
Show more news
Tech stack
Technologies named in job postings at Monte Carlo over the last 12 months, by layer, with the share of postings for each.
Technologies in use
23
Postings analysed
11
Stack modernity
88/100AI adopter
Languages
Python18%
JavaScript9%
Frameworks & libraries
OpenTelemetry9%
React.js9%
Databases
Snowflake91%
BigQuery9%
Google BigQuery9%
Cloud & platforms
Databricks100%
Anthropic91%
AWS18%
OpenAI9%
AI & ML
Claude100%
LLM27%
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Full Monte Carlo tech stack
23 technologies · by team · pay by technology · changes · similar stacks
Salary insights
Compensation ranges based on this company's open jobs
Median
$245k
Typical range
$100k – $400k
Open positions
6
5
Jobs
Junior
40%
Middle
20%
Senior
40%
| Level | Jobs | Median | Typical range |
|---|---|---|---|
| Junior | 2 | $250k | $100k – $400k |
| Middle | 1 | $260k | $260k |
| Senior | 2 | $250k | $230k – $270k |
Highest paying role
Enterprise Sales Manager, Americas
$400k
Normalized to USD per year
All salaries at Monte Carlo
By role, level and city, against the market · 6 jobs with pay
Growth
Hiring Momentum
67/100
Growing
Open positions
10
6 opened / 0 closed in 30 days
Median time-to-fill
89 days
faster than 15% of the market
ATS activity
Every ~4 hours
Today
Hiring Dynamics
+100%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Sales
70% ▲
AI/ML
10% ▼
Backend
10% ▲
Solutions
10% ▲
Actively hiring for Sales (+5 in 30 days)
Offices
Where the company hires and what each office is for
Cities
1
Hiring now
1
Countries
1
Busiest office
San Francisco
San Francisco
United States
HQ
1 open roles 1 posted in 90 days 100% remote 500-1000 est. staff $100k Avg Salary
hires for Sales
Jobs
$180k – $230k per year • Remote (United States) • Full-Time • 5+ years exp
Python
JavaScript
pySpark
Databases
Snowflake
Databricks
AI/ML
Claude
Spark
AI Agents
LLM
Anthropic
Agentic Workflows
Frontend
React.js
DevOps
AWS
Apply
$240k – $270k per year • Remote (AMER) • Full-Time • 3+ years exp
Snowflake
Databricks
Google BigQuery
BigQuery
AI/ML
Claude
dbt
AI Agents
Anthropic
DevOps
OpenTelemetry
Apply
$200k – $260k per year • Remote (AMER) • Full-Time • 3+ years exp
Python
Snowflake
Databricks
AI/ML
Claude
Model Context Protocol
dbt
AI Agents
LLM
RAG
Anthropic
LLM Guardrails
Apply