# Senior Applied Scientist - AI Platform

**Company:** [Datadog](https://hotfix.jobs/companies/datadog)
**Location:** Paris, France
**Role:** AI Research
**Experience:** 6+ years
**Skills:** Python, LLMs, Agentic AI, Post-Training Data, Llm Evaluation, Experiment Design, Statistics, Data Analysis, Distributed Systems, ML Infrastructure, Observability, Monitoring Systems, Generative Simulations
**Posted:** 2026-09-02

> The applied scientist will lead GenSim’s methodology for creating realistic, production-grade simulated environments and high-quality post-training data for Datadog agents. The role requires deep LLM and agent experience, evaluation expertise, Python, distributed systems, and the ability to set technical direction.

## Job Description

## Responsibilities
- Own the applied science direction for Generative Simulations (GenSim), setting methodology and technical direction for simulated environments and post-training data.
- Define, measure, and improve post-training data quality across correctness, representativeness of customer systems and production telemetry, and difficulty.
- Research and engineer realistic, imperfect production-like environments and challenging injected failures.
- Build scalable, production-grade synthetic-environment systems that are reliable and invokable within training loops.
- Determine how simulated data should be applied in LLM post-training and evaluation.
- Own evaluation approaches for agents and LLM applications in simulated environments.
- Collaborate with engineers and applied scientists across AI SRE, model training, and evaluation and experimentation teams.

## Requirements
- 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.
- Hands-on experience creating, managing, and controlling the quality of LLM and agent post-training data.
- Expertise in LLMs and agentic applications.
- Experience evaluating agents or LLM applications and defining evaluation criteria.
- Strong programming and production software engineering skills.
- Python and ability to ship scalable production systems and work with distributed systems.
- Strong cross-functional collaboration and technical decision-making skills.

## Nice-to-haves
- LLM fine-tuning, post-training, or model training experience.
- Statistics, experiment design, and data analysis.
- Production-level ML infrastructure deployment.
- Observability or monitoring systems.
- Architecture-level understanding of LLMs.

## Compensation and Benefits
- New-hire stock equity (RSUs) and employee stock purchase plan (ESPP).
- Continuous professional development, product training, and career pathing.
- Intra-departmental mentor and buddy program.
- Community Guild participation and Inclusion Talks.
- Spring Health benefits for employees and dependents aged 6+.
- Competitive global benefits, varying by country of employment and employment nature.

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