Applied Scientist, GenSim: Generative Simulation Platforms

Datadog; Paris; Hybrid; EUR 120,000 - 160,000; Full time

Benefits offered by this job

RSUs

ESPP

Professional development

Mentor program

Inclusion Talks

Spring Health

Job summary

Datadog seeks a seasoned Applied Scientist for GenSim to lead the methodology for simulated environments and post-training data used by LLMs. You will shape data quality, realism, and evaluation approaches, building scalable production systems rather than research scripts.

You will collaborate across Bits AI SRE, model training, and the broader evaluation pillar to ensure learnings translate across teams. A strong background in ML, data analysis, and scalable software is essential.

Qualifications

• 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 and quality control.

Responsibilities

• Own the applied science direction for GenSim: set the methodology and forward-looking calls on simulated environments and post-training data.
• Define, measure and raise the quality of post-training data against real distribution and production telemetry.
• Close the realism gap: make simulated environments look like real, imperfect production systems.
• Build scalable, production-grade systems—outputs are systems of synthetic environments, not just scripts.
• Determine how data is best applied in LLM post-training and evaluation, and own evaluation approaches for these environments.
• Work cross-functionally with Bits AI SRE, model training, and other teams to move learnings across.
• Collaborate across engineering and science teams, acting as domain expert to steer next steps.
• thrive in ambiguity and make sound technical calls when paths are undefined.

Skills

Applied ML engineering

LLM and agent post-training data

Distributed systems experience

Strong programming

Education

PhD or MS in a relevant field

Tools

Python

Distributed systems

Job description

Datadog seeks a seasoned Applied Scientist for GenSim to lead the methodology for simulated environments and post-training data used by LLMs. You will shape data quality, realism, and evaluation approaches, building scalable production systems rather than research scripts.

You will collaborate across Bits AI SRE, model training, and the broader evaluation pillar to ensure learnings translate across teams. A strong background in ML, data analysis, and scalable software is essential.