Staff ML Systems Engineer — GPU & Distributed Training

Digitailor; Menlo Park (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Digitailor is hiring to advance training and inference systems for image, video, and world-model workloads. You will optimize GPU kernels, distributed execution, and multi-node serving with Nsight-driven profiling.

Ideal candidates have 2+ years in ML systems, a CS degree, and hands-on experience writing kernels for NVIDIA GPUs using CUDA/CUTLASS/Triton/PTX. Visa sponsorship is available for qualified applicants.

Qualifications

• Two years of experience in deep learning training or inference systems.
• Hands-on ML systems or GPU performance work in current/most recent role.
• Experience writing kernels for NVIDIA GPUs using CUDA/CUTLASS/Triton/PTX.
• Authorship of core features in inference or training frameworks.
• Strong CS and ML fundamentals; degree in a quantitative field.

Responsibilities

• Optimize training and inference performance for image, video, diffusion, and world-model workloads.
• Profile bottlenecks at kernel, memory, system, and cluster levels using Nsight and related tools.
• Implement low-level GPU optimizations using CUDA and Triton.
• Design and improve distributed training and inference engines for diffusion models.
• Improve GPU-to-GPU communication with NCCL, RDMA, InfiniBand, or RoCE.
• Build benchmarks and regression tests to preserve performance gains in production.
• Contribute to hardware-aware kernel, runtime, and model co-design.

Skills

CUDA

Triton

CUTLASS

PTX

NCCL

RDMA

InfiniBand

RoCE

Nsight

Education

Bachelor's degree in Computer Science

Tools

NVIDIA Nsight

CUDA Toolkit

Triton Inference Server

CUTLASS

PTX

NCCL

RDMA

InfiniBand

RoCE

Job description

Digitailor is hiring to advance training and inference systems for image, video, and world-model workloads. You will optimize GPU kernels, distributed execution, and multi-node serving with Nsight-driven profiling.

Ideal candidates have 2+ years in ML systems, a CS degree, and hands-on experience writing kernels for NVIDIA GPUs using CUDA/CUTLASS/Triton/PTX. Visa sponsorship is available for qualified applicants.