AI Research Engineer - Datadog AI Research DAIR

Datadog, Inc.; Paris; On-site; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Competitive benefits

Stock equity (RSUs)

ESPP

Global collaboration

Conference attendance

Mentor program

Inclusive culture

Job summary

Datadog is seeking a research engineer to partner with researchers, turning ideas into production-ready systems. You will build data, tooling, and infrastructure to enable rapid iteration, trustworthy evaluation, and smooth transitions from prototype to production.

Join the AI Research team tackling Observability Foundation Models, SRE Autonomous Agents, and Production Code Repair Agents. You will implement models, run large-scale experiments, and collaborate with Research Scientists, Product,

Qualifications

• Strong software engineering skills with experience in observability, SRE, or security.
• Deep knowledge of distributed computing and ML systems for training and inference at scale; experience with Ray, Slurm, or similar frameworks is a plus.
• Proficient in Python; familiar with a systems language (Rust, C++, or Go).
• Experience implementing and operating ML training/inference systems (PyTorch or JAX); containerization, orchestration, and GPU acceleration.
• Familiar with efficient training, fine-tuning, and inference for large foundation models.
• Ability to explain design and performance trade-offs to technical and non-technical audiences.
• Strong interest in open science and open-source contributions, including sharing artifacts.

Responsibilities

• Build and operate datasets, training and evaluation pipelines, benchmarks, and internal tooling.
• Implement models, run experiments at scale, and profile for reliability, performance, and cost.
• Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery.
• Make the research stack observable, reproducible, and easier to use.
• Establish rigorous automated benchmarks and regression tests for forecasting, anomaly detection, multi-modal analysis, agents, and code repair tasks.
• Collaborate with Research Scientists, Product, and Engineering to integrate AI capabilities into Datadog’s product ecosystem and harden prototypes into reliable services.
• Contribute high-quality code, documentation, and open-source artifacts for the community and internal teams.

Skills

Python

Distributed systems

Open-source

ML systems

Cloud infrastructure

Communication

Tools

Ray

Slurm

Docker

Kubernetes

CUDA

Job description

As a 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 proven track record of AI-powered solutions (e.g., Bits AI, Watchdog, and Toto), Datadog AI Research is tackling high-risk, high-reward projects grounded in real-world challenges in cloud observability and security.

We are currently focused on three 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 agents (described below) to natively analyze telemetry data.
2. Site Reliability Engineering (SRE) Autonomous Agents – Creating AI agents to automatically detect, diagnose, and resolve incidents in production environments, pushing the boundaries of multi‑step planning, reasoning, and domain‑specific knowledge.
3. Production Code Repair Agents – Developing agents and models that leverage code, logs, runtime data, and other signals to identify, fix, and even preempt performance issues and security vulnerabilities in production code.

What You’ll Do:

• Build and operate datasets, training and evaluation pipelines, benchmarks, and internal tooling
• Implement models, run experiments at scale, and profile for reliability, performance, and cost
• Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery
• Make the research stack observable, reproducible, and easier to use
• Establish rigorous automated benchmarks and regression tests for forecasting, anomaly detection, multi‑modal analysis, agents, and code repair tasks
• Collaborate with Research Scientists, Product, and Engineering to integrate advanced AI capabilities into Datadog’s product ecosystem and to harden prototypes into reliable services
• Contribute high‑quality code, documentation, and open‑source artifacts that enable the community and internal teams to reproduce, extend, and evaluate results

Who You Are:

• You have strong software engineering skills with experience in domains such as observability, SRE, or security
• You have depth in distributed computing 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 you are 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 are familiar with efficient training, fine‑tuning, and inference techniques for large foundation models
• You can explain design and performance trade‑offs clearly to both technical and non‑technical audiences
• You have a strong interest in open‑science and open‑source contributions, including establishing rigorous benchmarks and sharing artifacts with the community

Bonus Points:

• You have a demonstrated ability to bridge cutting‑edge research prototypes and real‑world product applications, ideally with large foundation models, generative AI agents, or domain‑specific LLM deployments
• You are passionate about pushing the boundaries of AI while maintaining a strong focus on customer impact, scalability, and responsible deployment of new technologies
• You have hands‑on experience with GPU programming and optimization, including experience in CUDA
• You have experience writing production data pipelines and applications
• You have experience supporting or contributing to research publications

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 AI Research and want to grow your skills, we encourage you to apply.

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 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 other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.

Your Privacy:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.