Senior Applied Scientist - AI Platform

DataDog
Paris
Workplace: HybridFull timeFunction: Research & Scientific (R&D)Education: phdSkills: ["Collaboration","Technical leadership","Domain expertise","Ambiguity tolerance","Evaluation definition"]

Own applied science for GenSim, Datadog’s generative simulation environments and post-training data pipeline. Define methodology and quality measures for correctness, representativeness, and difficulty, then close the realism gap by making injected failures harder and more production-like. Build scalable, production-grade systems that generate synthetic environments for LLM post-training and evaluation, collaborating with adjacent Bits AI SRE and applied science teams to iterate release over release.

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DataDog
DataDog
3 days ago

Senior Applied Scientist - AI Platform

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Source: Company careers pageValidated by: Fursa AI
Last checked: 21 hours agoStatus: Live

Job Summary

Own applied science for GenSim, Datadog’s generative simulation environments and post-training data pipeline. Define methodology and quality measures for correctness, representativeness, and difficulty, then close the realism gap by making injected failures harder and more production-like. Build scalable, production-grade systems that generate synthetic environments for LLM post-training and evaluation, collaborating with adjacent Bits AI SRE and applied science teams to iterate release over release.
Location: Paris
Workplace: Hybrid
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Set applied science direction for GenSim, defining methodology and forward-looking technical calls for simulated environments and post-training data.
  • •Define, measure, and improve post-training data quality (correctness, representativeness, difficulty) with metrics the team can act on.
  • •Close the realism gap by driving research and engineering to make simulations less clean and injected problems harder.
  • •Build scalable, production-grade systems that reliably generate invokable synthetic environments inside a training loop.
  • •Own how these environments are applied in LLM post-training and evaluation, and partner across engineers and applied scientists to iterate approaches.

Pay and Benefits

Equity and Bonus:Equity
Perks:RsusEsppLearning Budget

Key Requirements

  • •PhD, MS, or equivalent research experience with strong applied mathematics grounding.
  • •6+ years of 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.
  • •Domain expertise in LLMs and agentic applications (not classical ML fine-tuning).
  • •Strong programming and production engineering skills; Python minimum and ability to ship scalable systems using distributed systems.
Experience:LLMAgentic applicationsPost-training dataSREObservabilityApplied research
Education:PhD / Doctorate
Skills:CollaborationTechnical leadershipDomain expertiseAmbiguity toleranceEvaluation definition
Languages:English
Tech Stack:PythonLLMsAgent post-training dataDistributed systemsGPU clustersTelemetryGPU

Company Brief

DataDog
Provides a cloud-native monitoring and observability platform that unifies metrics, traces, logs, and security signals to help engineering, operations, and security teams monitor and troubleshoot modern applications and infrastructure.
Industry: Developer Tools
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: New York, United States
Founded: 2010
Glassdoor
Glassdoor: 4.1
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