Lead Applied Scientist, Generative Simulation Environments

Datadog, Inc.; Paris; Híbrido; EUR 140.000 - 190.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Stock equity (RSUs)

ESPP

Professional development

Mentor and buddy program

Global benefits

Descripción de la vacante

Datadog is seeking a lead applied scientist for GenSim to shape the methodology for synthetic environments used in agent training and evaluation.

You will drive the quality and realism of post-training data, bridging the gap between simulated and real production telemetry, and collaborate across Bits AI SRE, model training, and broader evaluation teams to ensure usable, scalable environments in the training loop.

Formación

• 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.
• Hands-on experience with LLM and agent post-training data: creation, management, quality controls.

Responsabilidades

• Own the applied science direction for GenSim and define forward-looking methodology for simulated environments.
• Define, measure, and improve post-training data quality and representativeness for real production telemetry.
• Close the realism gap by driving research and engineering to make simulated environments resemble real systems.

Conocimientos

Applied mathematics

LLM engineering

Python

Production systems

Cross-functional collaboration

Educación

PhD, MS or equivalent research experience

Herramientas

Python

Git

Distributed systems

ML tooling

Descripción del empleo

Datadog is seeking a lead applied scientist for GenSim to shape the methodology for synthetic environments used in agent training and evaluation.

You will drive the quality and realism of post-training data, bridging the gap between simulated and real production telemetry, and collaborate across Bits AI SRE, model training, and broader evaluation teams to ensure usable, scalable environments in the training loop.