Staff Software Engineer

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 Avras platform.

You will work across the systems that turn Avras 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 youll work on

You will help design and scale the architecture behind Avras 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 Avras 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 youll 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 were 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

• Bachelors 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 Avras 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 wed like to meet you.

Required Experience:

Staff IC