Senior AI Research Engineer - LLM Training & Inference Infrastructure
Datadog is hiring a Senior AI Research Engineer - LLM Training & Inference Infrastructure in Paris, France. Level rates it ; you can apply on Datadog.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include vLLM and AI research.
- Level classifies it as a senior role.
- Level lists 400 other open roles at Datadog.
- The role was posted in the last few days.
AI in this role
As a Research Engineer on the Frontier Foundations team, you will partner with Research Scientists to build the systems that run large-scale LLM training, reinforcement learning, and inference at Datadog. You will build and operate an in-house distributed runtime that schedules work across large GPU clusters, and you will own how quickly researchers can iterate and how reliably long training runs complete.
Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model Toto), Datadog AI Research (DAIR) tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.
The Frontier Foundations team builds and maintains the high-performance infrastructure that lets us train and serve large language models at scale. It supports both of our research areas:
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:
- Design, build, and operate distributed training and inference infrastructure for large language models, including Mixture-of-Experts models
- Scale training with parallelism strategies (data, tensor, pipeline, and expert), memory optimization, checkpointing, and failure recovery
- Build inference and rollout systems that support evaluation, experimentation, and RL without slowing down training
- Build execution environments and sandboxes for online RL and agent post-training
- Own continuous performance validation: regular benchmarks of rollout speed, training throughput, and learning behavior, with dashboards that make regressions visible
- Improve the researcher experience: job submission, scheduling, quotas, GPU utilization, and observability
- Work with platform teams on cluster capacity, networking, and scheduling across GPU providers
- Investigate performance and reliability problems across hardware, software, and distributed systems
Who You Are:
- You have depth in distributed systems and ML infrastructure for training and inference at scale; experience with Ray, Kubernetes, Slurm, or similar frameworks is a plus
- You are proficient in Python and in at least one systems language (e.g., Rust, C++, or Go) ● You have experience operating large-scale training or serving infrastructure, or schedulers and orchestration systems for GPU or other high-performance workloads
- You understand what limits throughput/latency in distributed inference workloads: communication, memory, scheduling, and stragglers
- You have strong debugging, profiling, and incident-diagnosis skills, ideally including on-call experience
- You can explain design and performance trade-offs clearly to both technical and non-technical audiences
- Prior experience training machine learning models is valuable but not essential for engineers with strong distributed systems or HPC backgrounds
Bonus Points (any of the following):
- You are familiar with distributed training frameworks such as Megatron-LM, TorchTitan, or DeepSpeed
- You have experience with LLM inference engines such as vLLM or SGLang, including batching and KV-cache management
- You have experience with RL training systems and rollout infrastructure
- You have experience with collective communication (NCCL), RDMA, or low-latency networking
- You have experience with GPU benchmarking, profiling, and cluster performance monitoring
- You have experience building sandboxed code execution or large-scale evaluation infrastructure
- You have contributed to open-source ML or distributed systems projects
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 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. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
Equal Opportunity at Datadog:
Datadog is proud to offer equal employment opportunities 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.
#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. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
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.
How we rate this
Senior AI Research Engineer - LLM Training & Inference Infrastructure at Datadog rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- Walk me through how you've used vLLM in your day-to-day work.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI Research and vLLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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