Senior Applied Scientist - AI Platform

AI Platform builds the foundations of Datadog's AI efforts. The org is 70+ people organised in three pillars: training and serving (GPU clusters, distributed training, low-level infrastructure), agents (agent harnesses, memory systems, the internal AI gateway that routes every LLM request at Datadog), and evaluation...

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 representative traffic, injects ...

The team has no applied science support today and is learning post-training data methodology on the fly. That's the gap this role fills, and the open questions are the interesting part. How do you tell whether a generated environment is actually representative of the messy, incomplete telemetry real customers run — ...

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.

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.
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.