Member of Technical Staff, Research — Relational Foundation Models

Avra is a frontier AI lab building relational foundation models from the ground up (no third-party wrappers).

We model how businesses and individuals are connected and interact over time. Put simply: we bring connections into the math.

Leading enterprises in LatAm use Avra's models to power key decisions in credit, growth, and fraud. Our customers include the largest banks and marketplaces in Latin America, such as Mercado Libre, Telefonica, Banco Inter and Getnet.

Avra was founded in 2024 by Viviane Meister and Bruno Alano (ex-OpenAI). Our investors include NXTP, Clocktower Ventures, as well as the Sequoia and a16z scouts. We are accelerated by NVIDIA, AWS, and Google.

We are a team of builders. Join us.

Research at Avra

Foundation models learned to read text, images, and code. The data that runs the economy hasn't had its moment yet. It's structured: customers, accounts, transactions, and claims, stored in relational databases, connected as graphs, 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 foundation models for structured data, such as relational databases, graphs, and time series. Take a relational database: one pretrained model should read any company's database, understand its schema from the names of its tables and columns, and adapt to a new prediction task without a feature-engineering project or a model trained from scratch.

Avra exists to build those models and the platform that turns their predictions into decisions running in production, in our cloud or inside an enterprise's own infrastructure.

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. 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.

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

• Adapting to new tasks. How a pretrained model learns a task it has never seen, from examples in its context or through other mechanisms, what information it needs (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.

• Post-training. Fine-tuning and other ways to specialize the pretrained model to a customer's data and task, and knowing when each one is worth it.

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

• Methodological rigor: 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

1. Intro call (30 min). You, what you want to work on, and what we're building.

2. Deep dive (60 min). A project you're proud of, taken as deep as you want to go.

3. Research talk (60 min). You present past work to the team and take questions.

4. Founders (45 min).

5. 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.