---
title: 'Member of Technical Staff | Inference Platform at Avra'
canonical: 'https://feeny.ai/job/member-of-technical-staff-inference-platform-avra-sao-paulo-wxsgy40xg8y5'
type: 'job'
last_seen: '2026-09-26'
---

# Member of Technical Staff | Inference Platform 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/ff9dcca2-d812-44ec-98cb-f1abe5fb3c40

## 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 the Platform team to own where our models execute. Customers consume our models through large batches of millions of records and through real-time APIs, and they make business decisions on every response. You'll run governed model releases reliably and efficiently — in our cloud and on customer-hosted Kubernetes — and make inference fast, predictable, and cheap enough to serve both enterprise and mid-market customers.

## What you'll do

- Evolve Sophos, our online and batch inference runtime, built on Kubernetes.
- Run large batch inference on ephemeral jobs, with multi-dimensional admission control (CPU, memory, GPU).
- Build and extend the controller and its Kubernetes custom resources.
- Optimize each model's inference engine and feature processing.
- Serve graphs and data efficiently.
- Own execution of training, post-training, and fine-tuning jobs, in our cloud and in customer dataplanes / on-premisse cloud.
- Drive autoscaling, GPU serving, performance, and cost optimization, with telemetry for every model we run.

## How we measure success

- 99.9% serving availability.
- p95/p99 latency for online inference and throughput for batch.
- Cost per prediction and per training job.
- GPU utilization: paid capacity versus capacity actually used.
- Training and batch jobs that finish on time and succeed without manual retries.

## What we're looking for

- Experience running model serving or large-scale batch compute on Kubernetes.
- Experience building Kubernetes controllers or operators.
- Skill at profiling and optimizing data-heavy Python pipelines.
- A clear sense of cost: you treat compute efficiency as a product feature.
- Production-quality code and reviews, and a willingness to operate what you build.

## Nice to have

- Ray, Ray Serve, or KubeRay in production.
- Admission-control systems.
- GPU serving and performance optimization.
- Arrow, Parquet, Lance, or other columnar formats.
- Shipping software to customer-hosted Kubernetes.
- GCP/AWS and GKE/EKS, and financial services or 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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