About Avra

Avra is building relational foundation models for enterprise decision-making in Brazil.

Our work focuses on graph-native models for structured, high-stakes prediction problems: credit, fraud, growth, monitoring, and other decisions where entities cannot be understood in isolation. We model companies, people, and the relationships between them as evolving networks, then adapt those representations to customer-specific prediction tasks that plug into existing decisioning systems.

We work with internationally recognized research advisors, and we care about research that becomes useful in production. Our systems are already in production with large enterprise customers, supporting high-volume workflows where reliability, latency, model quality, and operational safety all matter.

The role

This is a senior individual contributor role for an engineer who can raise the technical quality, reliability, and architectural clarity of Avra’s platform.

You will work across the systems that turn Avra’s research and data assets into production infrastructure: APIs, model serving, batch inference, customer workspaces, data contracts, model lifecycle, observability, internal tools, and customer-specific deployments.

The role is not pure architecture. You will write code, review code, debug production systems, simplify designs, and help other engineers make better technical decisions. It is also not people management. Your leverage comes from technical judgment, execution quality, mentorship, and the systems you help shape.

You will collaborate closely with founders, product engineering, data platform, research, and customer-facing teams. The right person can move between product constraints, infrastructure constraints, ML constraints, and enterprise customer requirements without losing sight of what should be simple, reliable, and maintainable.

What you’ll work on

You will help design and scale the architecture behind Avra’s platform. We do not expect you to be an expert in everything on day one, but you should bring deep expertise in at least one core technical domain and strong architectural judgment across the others.

Backend and API systems: Design high-throughput, reliable services in Go, Python and Rust. Build API surfaces that expose relational intelligence safely and clearly.
Infrastructure and cloud: Strengthen production infrastructure across Kubernetes, GCP, AWS, observability, incident response, deployment workflows, and CI/CD.
Data and ML platform: Improve the systems that connect customer data, Avra’s knowledge graph assets, model outputs, and downstream enterprise workflows.
MLOps and inference: Scale model serving and batch inference. Improve model versioning, aliases, challenger and shadow deployments, rollback, monitoring, and customer-specific deployments.
Research and graph infrastructure: Build clean interfaces around internal systems such as graph training, sampling, embeddings, and feature pipelines so research components can become reliable production capabilities.
Engineering quality: Improve testing strategy, code review standards, operational readiness, technical design quality, and the way engineers reason about tradeoffs.
Technical mentorship: Help senior and mid-level engineers grow through design reviews, pairing, code reviews, clear written feedback, and pragmatic technical leadership.

The problems you’ll help solve

How should customer-specific models, data, and deployments be isolated while sharing common platform infrastructure?
How do we make batch inference, online inference, and model versioning feel like one coherent system?
How do we expose powerful relational intelligence through simple APIs and operationally safe customer workflows?
How do we keep systems debuggable when outputs depend on customer data, graph features, embeddings, model versions, and downstream integrations?
How do we help research move faster without letting experimental complexity leak into production?
How do we design platform abstractions that are strong enough for enterprise customers but simple enough for a small team to operate?

What we’re looking for

8+ years of software engineering experience, including significant ownership of production systems
Experience operating at staff, principal, tech lead, or equivalent senior IC level
Strong backend engineering experience with Go, Python, Rust, or similar languages
Strong understanding of distributed systems, APIs, cloud infrastructure, data-intensive applications, and production reliability
Experience designing systems that cross team boundaries and remain maintainable as teams and products grow
Comfort with large datasets, asynchronous processing, queues, batch jobs, object storage, and operational constraints
Ability to reason clearly about tradeoffs: simplicity vs. flexibility, velocity vs. reliability, abstraction vs. duplication, build vs. buy
Strong code review instincts and a high bar for correctness, readability, testing, and operational safety
Ability to mentor other engineers without relying on formal authority
Clear written and verbal communication, especially around technical decisions, risks, and tradeoffs

You stand out if

You have built infrastructure for ML platforms, data platforms, model serving, experimentation, or decisioning systems
You have worked with Kubernetes, Ray, GCP, AWS, BigQuery, Dagster, dbt, Lance, or similar systems
You have experience with model registries, batch inference, online inference, feature pipelines, or customer-specific ML deployments
You have worked in environments with reliability, security, auditability, or compliance requirements
You have written or maintained systems in Go, Python, Rust, or TypeScript at production scale
You have helped teams migrate from ad hoc systems to clearer platform abstractions
You can debug across layers: product behavior, API, database, queue, infrastructure, observability, data pipeline, and model output
You have worked closely with data scientists, ML researchers, data engineers, or enterprise customers
You care about building systems that improve real business decisions, not just systems that look clean in isolation

Requirements

8+ years of professional software engineering experience
Strong written English
Portuguese is useful, but not required
Experience working in remote or distributed teams
Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field, or equivalent practical experience

What we offer

Competitive salary, equity, and open compensation bands
Direct collaboration with founders and technical leadership
High ownership over systems that become part of Avra’s core platform
100% remote work, with a São Paulo office available when you want it
Flexible time off, national health plan, and extended parental leave
A technical environment spanning product engineering, data systems, ML infrastructure, and enterprise-scale deployment

If you want to shape the platform layer behind relational AI systems at a frontier AI lab, not as a clean architecture exercise but as infrastructure used by real enterprises to make better decisions, we’d like to meet you.

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