---
title: 'Member of Technical Staff, Research — Relational Foundation Models at Avra'
canonical: 'https://feeny.ai/job/member-of-technical-staff-research-relational-foundation-models-avra-remote-hq1dcbsmght9'
type: 'job'
last_seen: '2026-10-17'
---

# Member of Technical Staff, Research — Relational Foundation Models at Avra

- **Company:** Avra
- **Location:** Remote
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-04-20
- **Last confirmed live:** 2026-10-17
- **Apply:** https://jobs.ashbyhq.com/avra/0ccb2c8e-438d-4e97-bee0-c1ae390a02ac

## Job description

## About Avra

Foundation models learned to read text, images, and code. The data that runs the economy hasn't had its moment yet. It lives in relational databases: customers, accounts, transactions, and claims, linked by keys and moving through time. Every credit approval, fraud block, and retention offer is a prediction over that data. Today each one is still a bespoke model, rebuilt by hand for every task, schema, and company. We're building a foundation model for relational data: one model that reads any database, understands its schema from the names of its tables and columns, and learns a new task from labeled examples in its context, the way language models learned to follow a prompt. That means no training run per task and no feature-engineering project per customer. Avra exists to build that model and the platform that turns its predictions into decisions running in production, in our cloud or inside an enterprise's own infrastructure. We're an AI research lab with our home in São Paulo, backed by Sequoia and Andreessen Horowitz, and we ship with enterprise design partners in banking, payments, telecom, and commerce, including Mercado Livre, Inter, PicPay, Banco BV, and Vivo.

## About the team

We are a small group of researchers, engineers, and company builders, with our home in São Paulo and a team spread across San Francisco, Latin America, and Europe. Everyone technical at Avra is a Member of Technical Staff. We don't separate "researchers" from "engineers" before the problem is understood, so the people who design an experiment also write the code that runs it and own what happens next. We hire at all levels. The scope you take on comes from what you've shown, not from a title. Our researchers have published widely at venues like NeurIPS and ICLR. You can find our work on [our research page](https://avra.ai/research).

## Your mission

Help build a foundation model that understands any company's relational data and predicts well enough to run its decisions, better than the hand-built models companies rely on today.

## What you'll work on

- In-context learning over relational data. How the model uses labeled examples at inference time, what makes a good context (which rows, how much history, which neighbors), and why it fails when it fails.
- Pretraining from scratch. Architectures, objectives, and scaling for transformers over multi-table, temporal data.
- Pretraining data. Which corpora, real and synthetic, teach a model to generalize across schemas, domains, and languages.
- Time and leakage. Predictions that only see what was knowable at decision time. That's the difference between a benchmark number and a model a bank can trust.
- Evaluation. Relational and tabular benchmark suites, strong baselines (gradient boosting, tabular foundation models, task-specific relational models), and protocols that keep comparisons honest.
- Beyond the context window. When adapting or fine-tuning the model on a customer's database beats in-context prediction, and by how much.

## What we hope you'll bring

- Excellent fundamentals and implementation skills: you train models in PyTorch yourself, including multi-GPU runs you've debugged
- Research taste: you can read a paper, find the idea that matters, and design the cheapest experiment that could prove it wrong
- Experimental discipline: temporal splits, ablations, multiple seeds, strong baselines, and honest error analysis
- Experience with structured data, such as tabular or relational learning, recommender systems, time series, or any domain where gradient boosting is hard to beat
- Clear written English

Bonus points if you have

- Worked on tabular or relational foundation models, in-context learning, or meta-learning
- Pretrained transformers at scale, or studied their scaling behavior
- Written Rust, C++, CUDA, or Triton, or made a data loader or sampler substantially faster
- Shipped a model to production and lived with how it behaved afterwards
- A record of strong research, whether in papers or in open-source work

We welcome academic researchers and researchers with unconventional backgrounds. A degree is not a requirement.

## How we interview

- Intro call (30 min). You, what you want to work on, and what we're building.
- Deep dive (60 min). A project you're proud of, taken as deep as you want to go.
- Research talk (60 min). You present past work to the team and take questions.
- Founders (45 min).
- References.

Logistics

- Location: Remote. We hire in most countries through Deel. The team works across San Francisco, Brazil, Uruguay, and Europe, and we get together in person regularly, in São Paulo and elsewhere.
- Compensation: Competitive salary and equity, depending on background and level.
- Compute: GPUs for pretraining-scale experiments, plus a research budget.
- Benefits: Flexible time off, health coverage, extended parental leave, and travel to the conferences where you publish.

We're actively hiring for this role, and the research team reads every application. If we don't move forward this time, please wait six months before you apply again.

## 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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