AI Research Engineer - Datadog AI Research, Dair

As a Research Engineer on our team, you will partner with Research Scientists, working on fundamental research problems and collaborating with Datadog’s Product and Engineering teams to help translate research advances into tangible benefits for our customers.

Building on our proven track record of delivering AI-powered solutions, including Bits AI, Toto 2.0, and Scout, Datadog AI Research (DAIR) pursues ambitious, high-risk, high-reward research initiatives focused on solving real-world challenges in cloud observability and security.

We are currently focused on two key research areas:

1. Observability Foundation Models – Building state-of-the-art models for advanced forecasting, anomaly detection, and multi-modal telemetry analysis (logs, metrics, traces, etc.). These models will also provide the foundation for our decision models to natively analyze telemetry data.

2. Observability Native Decision Models  – Developing multimodal (logs, traces, metrics, text, images, ...) hybrid decision and reasoning models that are general and efficient enough to be watching your entire system continuously and effectively. Building and training hybrid Decision/Generative systems, e.g., decision models call on agentic models for deeper investigations or support evidence.

What You'll Do:

• Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling

• Implement models, run experiments at scale, and profile for reliability, performance, and cost

• Build simulation environments and replay infrastructure for agent training and evaluation

• Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery

• Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity

• Collaborate with Research Scientists, Product, and Engineering to integrate capabilities into Datadog's products and to harden prototypes into reliable services

• Share research findings with external audiences upon reaching significant project milestones.

Who You Are:

• You have 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

• You are proficient in Python, familiar with a systems language (e.g., Rust, C++, or Go), and comfortable with modern cloud and data infrastructure

• You have practical experience implementing and operating ML training and inference systems (e.g., PyTorch or JAX), including containerization, orchestration, and GPU acceleration

• You have 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 (quantization, speculative decoding)

• You can explain design and performance trade-offs clearly to both technical and non-technical audiences

• You have experience supporting or contributing to research publications

Bonus Points (any of the following):

• You have strong software engineering skills with experience in domains such as observability, SRE, or security

• You have experience bridging research prototypes and real-world product applications, especially with large foundation models, world models, or RL-trained agents

• You have a passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment

• You have hands-on experience with GPU programming and optimization, including CUDA

• You have experience writing production data pipelines and applications

• You have experience building simulation or sandbox environments for agent training

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

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