Senior Applied Scientist - AI Platform

Job summary

Responsibilities

Specifically, you'll be the first applied scientist on GenSim (Generative Simulations), the team that builds the environments Datadog's agents learn in. GenSim doesn't replay sampled telemetry — it stands up real, fully instrumented applications that talk to Datadog, drives them with…

• 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…
• Define, measure and raise the quality of post-training data — basic correctness, representativeness against the real distribution of customer systems and production telemetry…
• 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…
• 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…
• 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.

What they're looking for

• 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.
• 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…
• You have evaluated agents or LLM applications, and can define what 'good' means before you measure it.

Tech stack

• Python