AI Research Engineer – Datadog AI Research (DAIR)

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 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:

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.

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
Contribute to research publications at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and produce high-quality code, documentation, and open-source artifacts

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.

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.

About Datadog:

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

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.

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.

#LI-Hybrid

Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.

The reasonably estimated yearly salary for this role at Datadog is:

$140,000—$400,000 USD

About 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.

Privacy and AI Guidelines:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.

Details

Location

New York, NY, USA

Salary

$140,000 – $400,000/year

Technologies

We are focused on two 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.

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
Contribute to research publications at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and produce high-quality code, documentation, and open-source artifacts

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

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.

About Datadog:

Equal Opportunity at Datadog:

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.

#LI-Hybrid

The reasonably estimated yearly salary for this role at Datadog is:

$140,000—$400,000 USD

About Datadog:

Equal Opportunity at Datadog:

Privacy and AI Guidelines: