AI Research Engineer - Datadog AI Research (DAIR)

Gomyneed Private Limited; Singapore; Hybrid; SGD 146,000 - 262,000; Full time

Job summary

Datadog is seeking an AI Research Engineer for the Datadog AI Research (DAIR) group, based in Paris, France. The role focuses on world models for observability and trained agents, collaborating with researchers to turn ideas into working systems.

You will build and operate large-scale training pipelines, evaluation infrastructure, and tooling, while advancing models and simulation environments for agent training and evaluation. Prior experience with distributed RL and ML systems is essential.

Qualifications

• Experience in distributed computing for training and inference at scale.
• Proficiency in Python and systems language (Rust/C++, Go).
• Hands-on with ML training/inference systems (PyTorch/JAX), containers, GPU acceleration.
• Experience with large-scale model training and fine-tuning (Megatron-LM, DeepSpeed, RLHF).
• Experience contributing to research publications.

Responsibilities

• 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.

Skills

Distributed computing

RL infrastructure

Python programming

Systems languages

ML systems

Rust/C++/Go

Tools

PyTorch

JAX

Megatron-LM

DeepSpeed

Ray

Slurm

Job description

AI Research Engineer - Datadog AI Research (DAIR)

Full-time AI / ML

About this role

Datadog is hiring an AI Research Engineer for Datadog AI Research (DAIR) (Paris, France). This is an AI / ML role listed on the official Datadog careers site. DAIR focuses on world models for observability and trained agents for observability, partnering Research Engineers with Research Scientists to turn research ideas into working systems.

Key Responsibilities:

• 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

Requirements:

• Depth in distributed computing, RL infrastructure, and ML systems for training and inference at scale (Ray, Slurm or similar a plus)
• Proficiency in Python and familiarity with a systems language (Rust, C++, or Go)
• Practical experience operating ML training and inference systems (PyTorch or JAX), including containerization, orchestration, and GPU acceleration
• Experience with large-scale model training and fine-tuning (Megatron-LM, DeepSpeed, SFT, RLHF, efficient inference)
• Experience supporting or contributing to research publications

Salary: Competitive (not disclosed); includes RSUs and ESPP

Job Type: Full-time