ML Systems Engineer — Accelerate GPU Inference & Training

Aionia Group; Menlo Park (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Aionia Group in Menlo Park, CA is hiring a Member of Technical Staff, ML Systems to accelerate model training and inference across image, video, and world-model workloads. You will work with a founding team on kernels, runtimes, and distributed engines that power production-scale ML stacks.

You’ll optimize GPU performance, profile bottlenecks with Nsight, and implement low-level CUDA and Triton improvements.

Qualifications

• Worked on inference or training performance — GPU kernels, runtime, or distributed execution.
• Optimized diffusion, video, image, or other multimodal model workloads.
• Degree in Computer Science or a related quantitative field.

Responsibilities

• Optimize GPU and system performance for training and inference across image, video, and world-model workloads.
• Profile and remove bottlenecks at the kernel, memory, system, and cluster level using Nsight and related tooling.
• Write low-level optimizations in CUDA and Triton on code paths that run in production.
• Build distributed inference and training engines for diffusion models across multiple GPUs and nodes.
• Own communication performance — NCCL, RDMA over InfiniBand or RoCE, and disaggregated serving.
• Build benchmarking and regression harnesses so performance gains don't slide back in production.

Skills

GPU kernel optimization

Distributed systems

Profiling

Performance optimization

Multimodal models

Education

Bachelor's degree in Computer Science or related field

Tools

CUDA

Triton

PyTorch

Nsight

NCCL

RDMA / InfiniBand

Job description

Aionia Group in Menlo Park, CA is hiring a Member of Technical Staff, ML Systems to accelerate model training and inference across image, video, and world-model workloads. You will work with a founding team on kernels, runtimes, and distributed engines that power production-scale ML stacks.

You’ll optimize GPU performance, profile bottlenecks with Nsight, and implement low-level CUDA and Triton improvements.