Staff ML Systems Engineer - GPU & Distributed Training

true-talent-solutions-partners; Menlo Park (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

true-talent-solutions-partners is seeking an experienced engineer to join its AI systems team in Menlo Park, CA, to optimize high-performance training and inference infrastructure for image, video, and world-model workloads. On-site position focusing on GPU kernel optimization, CUDA/Triton, and distributed systems.

You will profile bottlenecks with Nsight, develop low-level GPU improvements, and help design scalable training/inference engines for diffusion models while coordinating across

Qualifications

• Bachelor's degree in Computer Science or a related quantitative field
• Experience with GPUs and ML systems is highly desirable
• Strong fundamentals in ML and CS with hands-on software experience

Responsibilities

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

Job description

true-talent-solutions-partners is seeking an experienced engineer to join its AI systems team in Menlo Park, CA, to optimize high-performance training and inference infrastructure for image, video, and world-model workloads. On-site position focusing on GPU kernel optimization, CUDA/Triton, and distributed systems.

You will profile bottlenecks with Nsight, develop low-level GPU improvements, and help design scalable training/inference engines for diffusion models while coordinating across