AI Research Scientist - Datadog AI Research (DAIR)

Datadog; Paris; Sur place; EUR 75 000 - 90 000; Plein temps

Avantages offerts par ce poste

Competitive global benefits

Stock equity and ESPP

Collaborative environment

Mentorship program

Résumé du poste

Datadog is seeking a Research Scientist in Paris to work on AI research in observability and security. The role involves conducting research in generative AI, training multimodal models, and collaborating with teams to integrate advanced capabilities into products.

The ideal candidate will hold a PhD and have expertise in reinforcement learning and deep learning, with a strong publication record. This position offers the chance to work with cutting-edge AI technology in an inclusive environment.

Qualifications

• Deep expertise in generative modeling and reinforcement learning.
• Experience designing deep learning models and agents.
• Track record of publications at top-tier conferences.

Responsabilités

• Conduct research in generative AI and machine learning.
• Train multimodal models using large-scale telemetry data.
• Collaborate with cross-functional teams on integration.

Connaissances

Generative modeling

Reinforcement learning

Deep learning

Machine Learning

Multimodal learning

Formation

PhD in Computer Science, Machine Learning, or related field

Outils

PyTorch

DeepSpeed

Megatron-LM

Description du poste

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.

Building on our track record of AI‑powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model), Datadog AI Research tackles high‑risk, high‑reward problems grounded in real‑world challenges in cloud observability and security.

We are focused on two research areas:

1. World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
2. Trained Agents for Observability -- Post‑training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.

What You’ll Do:

• 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

Who You Are:

• 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

Bonus Points (any of the following):

• 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

Benefits and Growth:

• Competitive global benefits
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
• Opportunity to attend and present at conferences and meetups
• Intra‑departmental mentor and buddy program for in‑house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

Equal Opportunity at Datadog:

Datadog is an affirmative action and equal opportunity employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.