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# Applied Scientist - AI Platform

- **Company:** Datadog
- **Location:** Paris, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Data Analysis, Monitoring of Systems, Machine Learning, Datadog, Transfer Learning, Large Language Models, Data Analytics, Machine Learning Operations
- **Published:** October 16, 2026
- **Apply:** https://www.themuse.com/jobs/datadog/senior-applied-scientist-ai-platform

## About the Role

* You have a PhD, MS or equivalent research experience in a scientific field, with strong applied mathematics grounding.
 * 6+ years of relevant applied science or ML engineering experience, including setting technical direction for others.
 * You have hands-on experience with LLM and agent post-training data: how it is created, managed, and how training-data quality is controlled. This is the requirement that matters most.
 * You have real domain expertise in LLMs and agentic applications - not classical ML fine-tuning. Fine-tuning classifiers or traditional models is a different problem from the one this team is solving.
 * You have evaluated agents or LLM applications, and can define what 'good' means before you measure it.
 * You are a strong programmer and production software engineer. Python at minimum, plus the ability to ship scalable production systems and work with distributed systems.
 * You collaborate well across engineering and science teams, and you're comfortable being the domain expert who decides what comes next.
 * You thrive in ambiguity and can make sound technical calls when the path isn't yet defined.

Bonus Points:

 * Hands-on LLM fine-tuning, post-training or model training experience.
 * Background in statistics, experiment design and data analysis.
 * Experience deploying production-level ML infrastructure.
 * Observability or monitoring systems background.
 * Architecture-level understanding of LLMs.

## Description

* Own the applied science direction for GenSim: set the methodology and the forward-looking technical calls on how simulated environments and post-training data should be built, on a team where that decision-making does not exist yet.
 * Define, measure and raise the quality of post-training data - basic correctness, representativeness against the real distribution of customer systems and production telemetry, and difficulty - and make those measures something the team can act on release over release.
 * Close the realism gap. Simulated environments today are too clean and the injected problems are not yet hard enough; you'll drive the research and the engineering that make them look like real, imperfect production systems.
 * Build scalable, production-grade systems rather than research scripts. The output is not just a dataset - it is a system of synthetic environments that must be reliable and invokable inside a training loop.
 * Determine how this data is best applied, in LLM post-training and in evaluation, and own the agent and LLM application evaluation approaches for these environments.
 * Work cross-functionally with the engineers and applied scientists on adjacent teams - Bits AI SRE, the model training effort, and the wider evaluation and experimentation pillar - so that what you learn moves freely in both directions.