AI Research Scientist - Datadog AI Research (DAIR)

Datadog · Paris, France · Dev Eng · listed April 18, 2025

The shape of it

Seniority
Not stated
Where
Hybrid
Requirements listed
5
Length
1,030 words

In the posting’s own words

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products.

What it asks for · 5

  • You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)
  • You have extensive experience designing and implementing deep learning models and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch)
  • You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
  • You are familiar with efficient training, post-training, and inference techniques for large foundation models
  • You can explain complex models and research findings to both technical and non-technical audiences

Also a plus

  • Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents
  • Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
  • Experience writing production data pipelines and applications
  • Hands-on experience with GPU programming and optimization, including CUDA

What the job covers

  • Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability
  • Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure
  • Design and build simulated environments and RL training loops for on-policy agent training and evaluation
  • Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products
  • Stay at the forefront of foundation models, world models, and RL-based agent research
  • Contribute to research publications, present at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks

Degree language

  • You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)

Tools and skills named

Cloud & infra
  • Datadog5×
  • Observability4×
  • Distributed systems
  • Site reliability
Models & research
  • Machine learning3×
  • CUDA
  • Deep learning
  • GPU
  • Inference
  • PyTorch
  • Reinforcement learning
Security & compliance
  • Security2×
Data
  • Data pipelines
Go to market
  • Forecasting
Ways of working
  • Cross-functional

Words the posting leans on

  • models17×
  • research11×
  • agents10×
  • training7×
  • experience5×
  • foundation models5×
  • learning5×
  • product5×
  • e.g4×
  • multimodal4×
  • observability4×
  • world models4×
  • infrastructure3×
  • applications2×
  • areas2×
  • autonomous agents2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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How this page was made

An automated read of a public job posting, fetched August 25, 2026 and last changed by Datadog on August 24, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.