Senior Site Reliability Engineer, AI Research

About the AI Research Team

The AI Research team at Algolia combines fundamental research with product engineering to deliver customer-facing AI-powered features.

The team is highly cross-functional, made up of PhD researchers, full-stack engineers, and infrastructure specialists working together to explore new ideas, validate impact, and bring successful research outcomes into production. While the work is research-driven, the output is real, customer-facing systems.

The Opportunity

We are looking for an embedded Senior Site Reliability Engineer to join the AI Research team as a full member of the group. In this role, you will support both the research and product-engineering aspects of the team by ensuring the stability, scalability, and operability of the infrastructure that enables this work.

This is a classic SRE role focused on cloud-first, service-oriented architectures running on Google Cloud Platform. While the team builds AI-powered systems, AI or ML experience is not required for this role. Our priority is strong SRE fundamentals, experience operating production services, and comfort working in an environment with ambiguity and high ownership.

You will play an important role in day-to-day execution as well as in longer-term (12-month) planning, helping shape how the team builds and operates its platforms over time.

What You’ll Work On

Platform Reliability & Enablement

• Support and evolve the reliability of platforms used by the AI Research team. Examples of our infrastructure work to date include:
  • A production inference service (embedding model serving API)
  • AI data feature store
  • Internal tools used for novel research and experimentation
  • Infrastructure that combines the above to enable offline testing of customer deployments to agentically discover configuration improvements.
• Ensure production services meet expectations for availability, latency, and operational readiness, particularly for systems that sit on customer-critical paths
• Design infrastructure and operational patterns that prioritize iteration speed while maintaining appropriate safeguards for production systems

Embedded Collaboration

• Work closely with researchers and engineers in a cross-functional setting, acting as an advisor on infrastructure, reliability, and operational concerns
• Participate directly in team planning and execution, from early exploration through production rollout
• Help researchers self-serve infrastructure safely and effectively, without becoming a bottleneck

Cloud Infrastructure & Operations

• Build and maintain Kubernetes-based services on GCP using infrastructure-as-code and GitOps (Terraform, ArgoCD)
• Own and improve CI/CD pipelines for services written primarily in Go, with some Python-based services
• Design and operate observability systems