---
title: 'Head of Research at Avra'
canonical: 'https://feeny.ai/job/head-of-research-avra-remote-fxy5w2jpeqxb'
type: 'job'
last_seen: '2026-10-17'
---

# Head of Research at Avra

- **Company:** Avra
- **Location:** Remote
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-06
- **Last confirmed live:** 2026-10-17
- **Apply:** https://jobs.ashbyhq.com/avra/348af983-ddaf-4265-bd9f-ace65313d264

## 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. 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. Research is a team of Members of Technical Staff working on one portfolio: pretraining, in-context learning, data, and evaluation for our relational foundation model. Research leadership is shared between two peers. Our Chief Scientist owns scientific direction and the standard of evidence. The Head of Research owns whether the team delivers. 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

Make Avra's relational foundation model the best in the world, and build the team that keeps it there.

## What you'll own

- The portfolio. What Research works on this cycle, what it stops, and who owns each initiative. You set this together with the Chief Scientist and are accountable for the outcome.
- The team. Hiring, developing, and allocating researchers, and making Avra the place where the best researchers in relational and tabular ML want to work.
- The cadence. Written hypotheses, research reviews, decision records, and an experiment ladder that runs small and cheap before it scales. Plus a compute budget that's spent on purpose.
- The interfaces. Research hands candidate models and their evidence to ML Systems for governed release. It works with our data teams on pretraining corpora and supports Forward Deployment when a customer's model needs adapting. You make those contracts explicit and keep them working.
- The commitments. What our models can promise a customer, by when, and on what evidence. You'll explain this clearly to executives, to customers' risk teams, and to our own commercial team.

You'll stay hands-on: reading code, running experiments, and going into the details of a training curve when something looks wrong.

## What we hope you'll bring

- You've led a research or applied ML team whose models ran in production and moved a real metric
- Deep technical credibility in modern deep learning, especially transformers, pretraining, and in-context learning
- Portfolio judgment: you know when to scale a promising idea and when to kill it, and you've done both
- You've hired and grown researchers who went on to do the best work of their careers
- Operating rigor: reproducibility, evaluation protocols, and compute budgets
- Clear written and spoken English

Bonus points if you have

- Pretrained foundation models at scale
- Worked on tabular or relational foundation models, in-context learning, or meta-learning
- Built models for credit, fraud, or other regulated decisions
- Portuguese, since our design partners are in Brazil
- A record of research or open-source work that other researchers build on

## How we interview

- Intro call with the CTO (45 min). Your path, and what you'd want this role to be.
- Deep dive (60 min). A team or research program you led: the bets, the results, and the calls you'd make differently.
- Portfolio exercise (90 min). We share context on where the model is today. You come back with a written plan for the next two quarters and walk the CTO and Chief Scientist through it.
- Research team (60 min). A session with the researchers you'd lead, without the founders in the room.
- 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 meaningful equity.
- Compute: A compute and research budget that you'll own.
- Benefits: Flexible time off, health coverage, and extended parental leave.

## 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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