Senior Applied Scientist - AI Platform
Core
Designing and building synthetic environments (GenSim) for training and evaluating Datadog's AI agents and LLMs by creating instrumented applications, injecting controlled failures, and generating post-training data.
Role type
Senior Applied Scientist (AI Platform / GenSim)
Builds
Scalable production systems of synthetic environments and post-training data corpus for agent training and evaluation
Domain
AI/ML, Observability, Generative Simulations, LLM Post-training
Deliverable
production ML models | product features
Required skills
LLM and agent post-training data methodology, applied mathematics, LLM and agentic application evaluation, production software engineering, distributed systems, Python
Preferred skills
LLM fine-tuning, statistics and experiment design, production ML infrastructure deployment, observability/monitoring systems background, architecture-level understanding of LLMs
Technologies
Python, distributed systems, LLM frameworks, synthetic environment tools
Responsibilities
Define methodology for building simulated environments and controlling post-training data quality, close the realism gap in injected failures, build scalable production-grade systems for training loops, determine application of data in LLM post-training and agent evaluation, collaborate cross-functionally with engineering and science teams
Seniority
Senior, hands-on IC with technical direction
