About the role
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.
Responsibilities, requirements, fit, evidence and preparation are organized here. The original posting stays available for final verification.
What they’re looking for
Select a requirement to inspect fit, evidence or application context.
- 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
Helpful, not always essential
- 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
How they work
- 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.
- #LI-hybrid About Datadog: Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale.
How to apply
- If you’re passionate about technology and want to grow your skills, we encourage you to apply.
- Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
Benefits & working conditions
- 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
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