---
title: 'Member of Technical Staff | ML Systems at Avra'
canonical: 'https://feeny.ai/job/member-of-technical-staff-ml-systems-avra-sao-paulo-zkye2zekbwm5'
type: 'job'
last_seen: '2026-09-26'
---

# Member of Technical Staff | ML Systems at Avra

- **Company:** Avra
- **Location:** São Paulo, Brazil
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-09-23
- **Last confirmed live:** 2026-09-26
- **Apply:** https://jobs.ashbyhq.com/avra/96929364-d560-464e-94f3-1df3cf5df3fb

## 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. 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.

## About Avra

## Company Overview
- **One-liner**: Avra is a frontier AI lab building a predictive platform for enterprise decisions, powered by a Graph Foundation Model that models the relational economy.
- **Entity Type**: Private (early-stage startup)
- **Headquarters**: São Paulo, Brazil
- **Founded**: 2024
- **Founders**: Bruno Alano (CEO, co-founder) and Viviane Meister (CTO, co-founder)

## Core Business
- **Primary industry**: Enterprise AI / Decision Intelligence (credit, fraud, growth, monitoring)
- **Target customers**: B2B, Enterprise (banks, fintechs, marketplaces, SMB lenders)
- **Mission**: “Model relationships over time. Improve the decision systems enterprises already run.”

## Products & Services
- **Avra Graph Foundation Model**: A pre-trained temporal knowledge graph covering Brazil’s economy – companies, individuals, events, ownership, judicial events, geography. Fine-tuned per customer workspace for credit scoring, fraud detection, and propensity modeling.
- **Avra API & SDK**: Managed endpoints (mTLS) for prediction and explanation, with tenant isolation and audit trails.
- **Avra Playground**: Browser-based environment to experiment with decision flows, replay traffic, and inspect evidence.
- **Enterprise Deployment**: Shadow deployments alongside incumbent models, with batch and real-time inference surfaces.

## Market Standing
- **Valuation / Market Cap**: Not publicly available
- **Key Metric**: Total funding amount not disclosed; backed by “frontier funds across two continents”
- **Notable Investors/Partners**: Not named explicitly, but investors are described as “frontier funds across two continents”. Team alumni include OpenAI, Stone, Itaú, McKinsey, Embraer, XP, HSBC, Santander.
- **Growth Signals**: Small team (~20 people) with 7 open roles; remote-first with HQ in São Paulo; pilot results showing 1.8× NII on Avra-scored cohort, +90% conversion lift, +5.4 p.p. ROC AUC on held-out test set.

## Competitive Advantages
- **Graph-native reasoning**: Models relationships over time rather than flat rows, capturing risk dimensions orthogonal to traditional features (18–22% correlation with existing features).
- **Inductive generalization**: Scores entities never seen before by reasoning through counterparties and graph position.
- **Brazil-native foundation**: Trained on local semantics (CNPJs, corporate groups, informal networks) and legal events.
- **Developer-first, enterprise-ready**: API, SDK, playground, tenant isolation, ISO 27001 in progress, LGPD compliant.

## Strategic Focus
- Expand the Graph Foundation Model platform across more enterprise decision use cases (credit, fraud, growth, monitoring).
- Grow the team (~20 today) with research scientists, data engineers, and deployment strategists.
- Maintain a “small on purpose” approach with deep stack ownership from data ingest to live decision.

## Why Work Here
- **Culture**: “Small team, deep stack, real ownership.” Engineers, scientists, and operators own the work end-to-end.
- **Remote / Hybrid**: Remote-first with hybrid options in São Paulo.
- **Team Background**: Colleagues from OpenAI, Stone, Itaú, McKinsey, Embraer, XP, HSBC, Santander.
- **Perks**: Frontier research that ships into production; publish-grade research with real business impact; no contractors on the model path.
- **Open Roles**: 7 positions including Research Scientist, Senior Data Engineer, Staff Software Engineer, Deployment Strategist, Marketing Lead, Enterprise Sales.

## Sources
1. [avra.ai/about](https://avra.ai/about)
2. [avra.ai/careers](https://avra.ai/careers)
3. [avra.ai](https://avra.ai/?r=0)
4. [avra.ai/en/about](https://avra.ai/en/about)
5. [jobs.ashbyhq.com/avra](https://jobs.ashbyhq.com/avra)

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