Staff Software Engineer

Avra Engineer 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

• 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.