Applied AI Engineer

Monte Carlo, Remote, Americas

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.

All jobs

Applied AI Engineer

Monte Carlo, Remote, Americas

Posted pay

$200K–$260K

a year

Compared with similar jobs

This base pay is above the median for ML and AI engineering jobs that are remote in the US with no level in the job title.

Median: $192K a year
Other jobs in the group: 166

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: Remote, Americas
Work type: Remote
Level: Not given
Experience: 3+ years
Skills in the job text: Python
Airflow
Databricks
dbt
Snowflake
Posted: Aug 27, 2026
Last check: 11 h ago
Job type: ML / AI engineering

Apply on the Monte Carlo site Sign in to save

The Monte Carlo site opens in a new tab.

Monte Carlo

Open tech jobs: 3
With posted pay: 100%

About Monte Carlo: funding, hiring and pay

Monte Carlo

About Monte Carlo: funding, hiring and pay

Check my fit

Compare your résumé with this job. See where you match, what is missing and which lines to change.

The role

What you need

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.

Applied AI Engineer

The role

Applied AI Engineer

The role

What you will do

What you need

This is not for you if

Why Monte Carlo

Beware of Imposter Recruiters and Job Scams

About Monte Carlo

Similar jobs at other companies

Senior AI Engineer