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Senior Applied Scientist - AI Platform

Datadog· AI Platform / Datadog AI
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Paris, France full-timePosted 1mo ago

About the role

Senior Applied Scientist role within Datadog's AI Platform, specifically the GenSim (Generative Simulations) team in the evaluation and experimentation pillar. Builds environments for agent learning, defines data quality and post-training data methodologies, and ships scalable production-grade synthetic environments for LLM post-training and evaluation.

Responsibilities

  • Own the applied science direction for GenSim: set the methodology and 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. Drive the research and engineering to make simulated environments look like real, imperfect production systems.
  • Build scalable, production-grade systems rather than research scripts. Output is a system of synthetic environments 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 engineers and applied scientists on adjacent teams to ensure knowledge moves across boundaries.

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: how it is created, managed, and how training-data quality is controlled
  • Real domain expertise in LLMs and agentic applications (not classical ML fine-tuning)
  • Experience evaluating agents or LLM applications and defining quality criteria
  • Strong programming and production software engineering skills (Python at minimum) and ability to ship scalable production systems and work with distributed systems
  • Collaborative across engineering and science teams and comfortable being the domain expert
  • Ability to thrive in ambiguity and make sound technical calls in undefined paths

Skills

Required:PhD, MS or equivalent research experienceApplied mathematicsML engineeringLLM/post-training data experience

Benefits

  • New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
  • Continuous professional development, product training, and career pathing
  • Intra-departmental mentor and buddy program
  • Inclusive company culture and Community Guilds
  • Inclusion Talks, internal panel discussions
  • Free, global Spring Health benefits for employees and dependents age 6+
  • Competitive global benefits
Last updated Oct 22, 2026. Interested? Apply on the company site.

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