Head of Research

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 think that ends with a foundation model for relational data: one model that reads any database, understands its schema from the names of its tables and columns, and predicts from labelled examples shown to it in 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 a frontier lab with its 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.

Your mission

Make Avra's relational foundation model the best in the world at predicting from context, 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 relational and tabular ML researchers in Latin America 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 rigour: 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
  
  

Logistics

• Location: Remote, from anywhere. The team works across San Francisco, Brazil, Uruguay, and Europe, and we have an office in São Paulo.
• Compensation: Competitive salary and meaningful equity.
• Compute: H100-class GPUs and a research budget that you'll own.
• Benefits: Flexible time off, health coverage, and extended parental leave.
• Language: We work and interview in English.

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