Senior AI Research Engineer - GPU Systems & Kernels

As a Senior AI Research Engineer on the Frontier Foundations team at Datadog, 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, 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. This supports two 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.

Responsibilities:

• 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 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.

Requirements:

• Depth in at least one of: GPU kernel development and optimization (CUDA or similar), low-latency communication and collective operations, or large-scale training infrastructure.
• Ability to reason about hardware constraints and explain bottlenecks.
• Proficiency in a systems language (e.g., C++, Rust, or CUDA C) and in Python.
• Strong debugging and profiling skills, including tools such as Nsight.
• Ability to explain design and performance trade-offs clearly to both technical and non-technical audiences.

Nice to Have:

• Experience with Mixture-of-Experts models, expert parallelism, or sparse model inference.
• Familiarity with GPU communication libraries such as NCCL or NVSHMEM, and with compute/communication overlap.
• Experience with Triton, CUTLASS, or similar kernel frameworks.
• Familiarity with distributed training frameworks such as Megatron-LM, TorchTitan, or DeepSpeed.
• Experience building GPU-intensive systems in graphics, rendering, simulation, or scientific computing.
• Contributions to open-source ML or systems infrastructure projects.