БразилияMember of Technical Staff | ML Systems
3 нед. назад
БразилияLeadRemote
cudadistributed traininggraph neural networksarrowdata formatsexperiment trackingmodel versioning
Build and maintain machine learning systems infrastructure for reliable model training, evaluation, and deployment.
Обязанности
- At , every technical IC is a Member of Technical Staff (MTS). The title doesn't put anyone in a silo: you own systems and outcomes, not steps in a function, and you keep building depth in your area. Seniority shows up in your scope, level, and compensation, not in titles.
- In this role, you'll join our ML Systems team, which owns 's ML core and the governance of every model we ship. Research produces candidate models and evidence; you build the reliable path from data and training to a governed, reproducible release that can run in our cloud or in any customer environment. ML Systems is an internal platform: its users are our researchers and platform engineers, and its success is measured by the leverage it creates for them.
- 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 training performance.
- Specify our binary data formats (Lance, Arrow, CSR/CSC), and own materializations and feature backfills for training and evaluation.
- Define data contracts and consumption requirements with the teams that build our customer and proprietary datasets.
- Build and operate experiment tracking, checkpoints, and evaluation infrastructure, with reproducibility by default.
- Own the model registry, lineage, versioning, and compatibility across models, embeddings, and downstream models.
- Define and run release gates, so every model running in production, batch, or on-premise maps to a governed release.
- Make it possible to audit exactly which data, code, configuration, and evidence produced each release.
Требования
- 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 reproducibility.
- A product mindset: you treat an internal platform as a product with real users. You don't need to be a data scientist.
Будет плюсом
- 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.
Другое
- Time-to-experiment: how quickly a researcher goes from a hypothesis to materialized data, compute, and tracking.
- Time-to-governed-release: how quickly a validated candidate becomes an authorized release.
- Training throughput per GPU on our foundation model training runs.
- 100% of production models with complete release records and lineage — no ad hoc models in any environment.
- Every release reproducible from its registered data, code, and configuration.