Member of Technical Staff | ML Systems

📋 Description

• Build CUDA kernels and compute primitives for training and serving graph neural networks (GNNs).
• Evolve Monad, our sampler and distributed-training library, including neighbor sampling and
• Specify our binary data formats (Lance, Arrow, CSR/CSC), and own materializations and feature
• Define data contracts and consumption requirements with the teams that build our customer and
• Build and operate experiment tracking, checkpoints, and evaluation infrastructure, with
• Own the model registry, lineage, versioning, and compatibility across models, embeddings, and

🎯 Requirements

• Strong systems engineering skills and production-quality Python.
• Experience with distributed training (e.g., Ray, PyTorch distributed) and multi-node GPU workloads.
• Experience with columnar data formats and large-scale data materialization.
• Familiarity with ML lifecycle tooling: experiment tracking, model registries, evaluation, and
• A product mindset: you treat an internal platform as a product with real users. You don't need to

🎁 Benefits

• CUDA kernel development or GPU performance optimization.
• Graph neural networks or graph sampling at scale.
• Lance, Arrow, or other columnar/indexed storage formats.
• Multi-cloud GPU compute (e.g., SkyPilot).
• Model governance or audit requirements in financial services or other regulated environments.