Machine Learning Systems Engineer

Digital Waffle; Menlo Park (CA); On-site; USD 350,000 - 550,000; Full time

Benefits offered by this job

Equity

Job summary

Digital Waffle is seeking a Member of Technical Staff, ML Systems in San Francisco (On-site). You will optimize GPU and system performance for image, video and world-model workloads, and build distributed inference and training engines across multiple GPUs.

You will profile bottlenecks with Nsight, write low-level CUDA and Triton code, and own communication performance (NCCL, RDMA over InfiniBand/RoCE) to accelerate production workloads, reporting directly to the CEO.

Qualifications

• 1+ years in deep learning training or inference systems, or distributed systems.
• Hands-on experience with CUDA, Triton, PyTorch, NCCL or RDMA.
• Excited about shaving milliseconds off production workloads.

Responsibilities

• Optimize GPU and system performance for image, video and world-model training and inference.
• Profile and remove bottlenecks at kernel, memory, system and cluster level using Nsight and related tooling.
• Write low-level CUDA and Triton optimizations 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/RoCE, and disaggregated serving.
• Build benchmarking and regression harnesses so performance gains stick.

Skills

Deep learning systems

Distributed systems

Performance optimization

Tools

CUDA

Triton

PyTorch

NCCL

RDMA

Job description

Member of Technical Staff, ML Systems | San Fransisco (On-site) | $350K–$550K + Equity

Would you rather make a video model 10x faster than train one?

We're partnering with a seed-funded AI infrastructure startup that's rebuilding the training and inference stack for world models, from the GPU kernels all the way up to distributed serving. Today's ML stack was built for language models. This team is rebuilding it for image, video and world-model workloads, and their public benchmarks already show order-of-magnitude speedups at a fraction of the cost.

The team is five cofounders with deep expertise across distributed systems, kernel optimization, cloud infrastructure and research. You'd be one of the first engineering hires, reporting directly to the CEO.

What you'll do:

• Optimize GPU and system performance for image, video and world-model training and inference
• Profile and remove bottlenecks at the kernel, memory, system and cluster level using Nsight and related tooling
• Write low-level CUDA and Triton optimizations 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/RoCE, and disaggregated serving
• Build benchmarking and regression harnesses so performance gains stick

What we're looking for:

• 1+ years in deep learning training or inference systems, or distributed systems
• Hands-on experience with CUDA, Triton, PyTorch, NCCL or RDMA
• Someone who gets genuinely excited about shaving milliseconds off production workloads