Skill Compatibility Diagnostics
Live technical match evaluated against role architecture & expected skills.
You have foundational skills, but may need to brush up on a few key required domains before the technical loop.
Your skills are processed strictly in-memory and never saved to a database.
About the Role
As an AI Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production.
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.
Our Core Research Areas:
- 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.
- 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.
Join our collaborative, pragmatic, and thoughtful people-first community in Paris to solve tough problems, take smart risks, and shape the future of AI-driven cloud observability.
Key Technical Responsibilities
- check_circle Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling
- check_circle Implement models, run experiments at scale, and profile for reliability, performance, and cost
- check_circle Build simulation environments and replay infrastructure for agent training and evaluation
- check_circle Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery
- check_circle Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity
- check_circle Collaborate with Research Scientists, Product, and Engineering to integrate capabilities into Datadog's products and to harden prototypes into reliable services
- check_circle Contribute to research publications at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and produce high-quality code, documentation, and open-source artifacts
Prerequisites & Qualifications
- check Depth in distributed computing, RL Infra, and ML systems for training and inference at scale; experience with Ray, Slurm, or similar frameworks is a plus
- check Proficiency in Python, familiar with a systems language (e.g., Rust, C++, or Go), and comfortable with modern cloud and data infrastructure
- check Practical experience implementing and operating ML training and inference systems (e.g., PyTorch or JAX), including containerization, orchestration, and GPU acceleration
- check Practical experience with large-scale model training and fine-tuning, including frameworks like Megatron-LM, DeepSpeed, SkyRL, VeRL, or TorchTitan, and techniques such as SFT, RLVR, RLHF, and efficient inference
- check Ability to explain design and performance trade-offs clearly to both technical and non-technical audiences
- check Experience supporting or contributing to research publications
About Datadog Greenhouse Board
Verified hiring organization details.
Datadog is an observability and security platform for cloud applications, providing infrastructure monitoring, application performance monitoring, log management, and user experience tracking. Headquartered in New York, New York, the technology company enables organizations to automate cloud monitoring, secure infrastructure, and gain real-time visibility into complex tech stacks. The company uses Greenhouse as its primary platform to manage global candidate sourcing and talent acquisition.