About the job
We are seeking an AI software Engineer to join our team. This role focuses on maximizing performance and efficiency of large-scale AI training/RL/inference workloads on AMD GPU platforms. You will drive innovations across the full software-hardware stack, optimizing at scale and pushing the limits of system throughput, scalability, and utilization for generative AI workloads.
Responsibilities
Lead performance optimization of large-scale AI training/RL/inference workloads on AMD GPU platforms across single-node and multi-node environments.
Identify and eliminate system bottlenecks across compute, memory, and communication (e.g., kernel efficiency, memory bandwidth, network utilization).
Drive cross-stack optimizations spanning kernels, compilers, runtimes, communication libraries, and ML frameworks.
Develop and apply advanced profiling, benchmarking, and performance modeling methodologies.
Collaborate with hardware, compiler, and framework teams to influence next-generation GPU architecture and software stack design.
Contribute to and lead open-source efforts to improve ecosystem performance on AMD platforms.
Stay at the forefront of advancements in large-scale systems and performance optimization techniques.
Qualifications
Minimum
No minimum qualifications listed.
Preferred
Deep expertise in GPU architecture and performance characteristics (compute units, memory hierarchy, interconnects such as PCIe/Infinity Fabric/RDMA).
Strong experience with performance profiling tools (e.g., ROCm tools, Nsight-like systems, custom profilers) and bottleneck analysis.
Proven experience optimizing large-scale distributed training workloads across thousands of GPUs.
Experience with frameworks such as Megatron-LM, Torchtitan, vLLM, Sglang, or equivalent.
Strong understanding of communication libraries and patterns (e.g., NCCL/RCCL, collective ops, overlap of compute and communication).
Proficiency in Python and at least one systems language (C++/CUDA/HIP), including debugging and low-level optimization.
Experience with compiler stacks, kernel optimization, or graph-level optimization is a strong plus.
Demonstrated technical leadership and ability to influence cross-functional team