Senior AI Research Engineer - GPU Systems & Kernels
Datadog is hiring a Senior AI Research Engineer - GPU Systems & Kernels in Paris, France. Level rates it ; you can apply on Datadog.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions AI research among its tools and skills.
- Level classes it as a senior role.
- Datadog has 400 other open roles on Level.
- 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 make large-scale model training and inference as fast and efficient as the hardware allows. You will work at the lowest level of our stack, writing and tuning GPU kernels and removing the memory and communication bottlenecks that limit large Mixture-of-Experts models on the latest GPUs.
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.
Frontier Foundations 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:
- Write, tune, and debug GPU kernels (CUDA, Triton, or similar) for the operations that dominate LLM training and inference.
- Optimize for the latest NVIDIA GPU generations, including memory hierarchy, tensor cores, and low-precision formats such as FP8 and FP4
- Reduce communication cost in distributed workloads (all-to-all, all-reduce) and overlap communication with compute across NVLink and across nodes
- Profile end-to-end training and rollout workloads to identify whether they are compute-, memory-, or communication-bound, and quantify the headroom
- Build benchmarks, profiling tools, and regression tests that guide optimization decisions
- Collaborate with Research Scientists to turn new model architectures into efficient implementations
- Explore emerging techniques such as low-precision training and serving, speculative decoding, and fused or persistent kernels
Who You Are:
- You have depth in at least one of: GPU kernel development and optimization (CUDA or similar), low-latency communication and collective operations (e.g., RDMA, NCCL), or large-scale training infrastructure
- You can reason about hardware constraints (memory, bandwidth, interconnect topology) and explain where a bottleneck is and how you would confirm it
- You are proficient in a systems language (e.g., C++, Rust, or CUDA C) and in Python
- You have strong debugging and profiling skills, including tools such as Nsight
- You can explain design and performance trade-offs clearly to both technical and non-technical audiences
Bonus Points (any of the following):
- You have experience with Mixture-of-Experts models, expert parallelism, or sparse model inference
- You are familiar with GPU communication libraries such as NCCL or NVSHMEM, and with compute/communication overlap
- You have experience with Triton, CUTLASS, or similar kernel frameworks
- You are familiar with distributed training frameworks such as Megatron-LM, TorchTitan, or DeepSpeed
- You have built GPU-intensive systems in graphics, rendering, simulation, or scientific computing
- You have contributed to open-source ML or systems infrastructure 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.
#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 - GPU Systems & Kernels at Datadog rates 99 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?
- 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. 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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