Member of Technical Staff | ML Systems

Avra; São Paulo; On-site; BRL 350,000 - 520,000; Full time

Job summary

Avra is seeking a Senior MTS to join our ML Systems team, owning the end-to-end path from data to governed releases for models running in cloud, on-prem, or customer environments.

You’ll contribute to training pipelines, model governance, and reproducible experiments, with a focus on reliable, scalable ML infrastructure and governance practices.

Qualifications

• Strong systems engineering and production-grade Python skills.
• Experience with distributed training on multi-node GPUs.
• Familiarity with columnar data formats and large-scale data materialization.
• Knowledge of ML lifecycle tooling and model governance.

Responsibilities

• Build CUDA kernels and compute primitives for training and serving graph neural networks.
• Evolve Monad, a sampler and distributed-training library, for neighbor sampling and performance.
• Define data contracts and materializations for training and evaluation.
• Develop and operate experiment tracking, checkpoints, and reproducibility infrastructure.
• Own the model registry, lineage, and compatibility across models.
• Define release gates to map production models to governed releases.
• Audit data, code, configuration, and evidence for each release.

Skills

Systems engineering

Python

Distributed training

Columnar formats

ML tooling

Tools

Ray

PyTorch Dist

GNN frameworks

Job description

About The Role

At Avra, 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.

About The Role

At Avra, 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 Avra'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.

What you’ll do

• 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.

How we measure success

• 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.

What we’re looking for

• 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.

Nice to have

• 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.