Research Engineer - AI Performance & Kernel Optimization

As a Research Engineer - AI Performance & Kernel Optimization, you will improve and optimize the performance of our large-scale language model training and inference stacks. You will work closely with our pretraining and inference teams to identify bottlenecks, design and implement highly optimized kernels, and push the limits of throughput, latency, and hardware utilization across a range of accelerator platforms. This role is suited for someone who enjoys deep systems work, cares about performance at every level of the stack, and is excited to translate low-level optimizations into meaningful gains for frontier-scale AI systems.

Kernel development and optimization for large-scale ML workloads, using any level of the stack from PTX/assembly to CUDA, HIP, Triton, or other GPU DSLs

Performance tuning for training and inference stacks across GPUs and other accelerators

Profiling and eliminating bottlenecks in memory movement, communication, scheduling, and compute utilization

Optimizing distributed training and inference systems for large MoE models, including large-scale model parallelism

Portability and optimization across non-NVIDIA hardware, with special interest in AMD hardware such as the MI300x and MI355x

Collaboration with research and infrastructure teams to turn systems improvements into real-world model training and inference gains

Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued

We strongly value new and crazy ideas and are very willing to bet big on new ideas

We move as quickly as we can; we aim to minimize the bar to impact as low as possible

We all enjoy what we do and love discussing AI

Strong engineering aptitude for building reliable, high-performance systems

Excellent low-level performance intuition and the ability to reason about hardware-software interactions

Are excited to rapidly learn new systems, tools, and hardware environments

Excellent communication and collaboration skills, with the ability to work effectively across research and engineering teams

Enjoy diving deep into the weeds and hunting down the last 10–20% of performance

Experience writing highly performant GPU kernels at any level of abstraction–PTX, CUDA, HIP, Triton, or other kernel DSLs

Experience optimizing ML workloads for large-scale training, ideally in language model pretraining or inference environments

Experience with non-NVIDIA accelerator hardware, such as AMD, AWS Trainium, Google TPU, Qualcomm, ARM, Intel, and custom ASICs

Strong understanding of distributed training systems and parallelism schemes, including data parallelism, tensor/model parallelism, pipeline parallelism, sharding, and communication/computation overlap

Experience with performance engineering in other demanding parallel computing environments such as HPC, quantitative finance, scientific computing, graphics, compilers, or numerical simulation

Strong systems intuition around memory hierarchy, bandwidth constraints, kernel fusion, launch overhead, communication overhead, and hardware utilization

Experience using profiling and debugging tools to drive performance improvements

Familiarity with infrastructure underlying large-scale training and inference, including collective communication libraries, and runtime performance analysis

Background in a highly technical field such as physics, mathematics, theoretical computer science, computer science, or electrical engineering

Any HPC experience is a strong plus