Member of Technical Staff – ML Systems

Job not on LinkedIn

🔥 6 minutes ago

🇧🇷 Brazil – Remote

⏰ Full Time

🔴 Lead

🖥 Software Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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avra

WebsiteLinkedInAll Job Openings

1 - 10 employees

💼 Consulting

💸 Finance

🤝 B2B

Consulting • Finance • B2B

Avra is a venture capital firm dedicated to empowering growth stage founders by providing the essential support needed to scale their companies after Series A funding. With a commitment to building a prosperous future together, Avra focuses on strategic investments that foster innovation and entrepreneurship.

📋 Description

• Build CUDA kernels and compute primitives for training and serving graph neural networks (GNNs) • Evolve Monad, the sampler and distributed-training library, including neighbor sampling and training performance • Specify binary data formats (Lance, Arrow, CSR/CSC), and own materializations and feature backfills for training and evaluation • Define data contracts and consumption requirements with teams building 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 for governed production, batch, and on-premise model releases • Enable auditing of the data, code, configuration, and evidence that produced each release • Improve time-to-experiment, time-to-governed-release, GPU training throughput, release records, lineage, and reproducibility

🎯 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 reproducibility • A product mindset: treat an internal platform as a product with real users • CUDA kernel development or GPU performance optimization (nice to have) • Graph neural networks or graph sampling at scale (nice to have) • Lance, Arrow, or other columnar/indexed storage formats (nice to have) • Multi-cloud GPU compute (e.g., SkyPilot) (nice to have) • Model governance or audit requirements in financial services or other regulated environments (nice to have)