Senior Site Reliability Engineer, AI Research

<div class="content-intro"><p>At Algolia, we’re proud to be a pioneer and market leader in AI Search, empowering 17,000+ businesses to deliver blazing-fast, predictive search and browse experiences at internet scale. Every week, we power over 30 billion search requests — four times more than Microsoft Bing, Yahoo, Baidu, Yandex, and DuckDuckGo combined.</p> <p>In 2021, we raised $150 million in Series D funding, quadrupling our valuation to $2.25 billion. This strong foundation enables us to keep investing in our market-leading platform and serving incredible customers like Under Armour, PetSmart, Stripe, Gymshark, and Walgreens.</p></div><h4><strong>About the AI Research Team</strong></h4> <p>The AI Research team at Algolia combines <strong>fundamental research</strong> with <strong>product engineering</strong> to deliver customer-facing AI-powered features.</p> <p>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.</p> <h4><strong>The Opportunity</strong></h4> <p>We are looking for an <strong>embedded Senior Site Reliability Engineer</strong> 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.</p> <p>This is a <strong>classic SRE role</strong> focused on <strong>cloud-first, service-oriented architectures</strong> running on Google Cloud Platform. While the team builds AI-powered systems, <strong>AI or ML experience is not required</strong> for this role. Our priority is strong SRE fundamentals, experience operating production services, and comfort working in an environment with ambiguity and high ownership.</p> <p>You will play an important role in <strong>day-to-day execution</strong> as well as in <strong>longer-term (12-month) planning</strong>, helping shape how the team builds and operates its platforms over time.</p> <h4><strong>What You’ll Work On</strong></h4> <h4><strong>Platform Reliability &amp; Enablement</strong></h4> <ul> <li>Support and evolve the reliability of platforms used by the AI Research team. Examples of our infrastructure work to date include:</li> <ul> <li>A production inference service (embedding model serving API)</li> <li>AI data feature store</li> <li>Internal tools used for novel research and experimentation</li> <li>Infrastructure that combines the above to enable offline testing of customer deployments to agentically discover configuration improvements.<br><br></li> </ul> <li>Ensure production services meet expectations for availability, latency, and operational readiness, particularly for systems that sit on customer-critical paths<br><br></li> <li>Design infrastructure and operational patterns that prioritize <strong>iteration speed</strong> while maintaining appropriate safeguards for production systems<br><br></li> </ul> <h4><strong>Embedded Collaboration</strong></h4> <ul> <li>Work closely with researchers and engineers in a cross-functional setting, acting as an advisor on infrastructure, reliability, and operational concerns<br><br></li> <li>Participate directly in team planning and execution, from early exploration through production rollout<br><br></li> <li>Help researchers self-serve infrastructure safely and effectively, without becoming a bottleneck<br><br></li> </ul> <h4><strong>Cloud Infrastructure &amp; Operations</strong></h4> <ul> <li>Build and maintain Kubernetes-based services on GCP using infrastructure-as-code and GitOps (Terraform, ArgoCD)<br><br></li> <li>Own and improve CI/CD pipelines for services written primarily in Go, with some Python-based services<br><br></li> <li>Design and operate observability systems using tools such as Datadog<br><br></li> <li>Participate in an on-call rotation (relatively light), responding to incidents and helping improve systems over time<br><br></li> </ul> <h4><strong>What We’re Looking For</strong></h4> <h4><strong>Required Experience</strong></h4> <ul> <li>Strong experience operating <strong>cloud-first infrastructure</strong><br><br></li> <li>Hands-on experience running production services on Kubernetes<br><br></li> <li>Proficiency with infrastructure-as-code (Terraform) and CI/CD systems<br><br></li> <li>Experience supporting production services written in <strong>Go</strong> (Python experience is a plus)<br><br></li> <li>Solid grounding in service reliability, incident response, and operational best practices<br><br></li> <li>Comfort working in environments with ambiguity, where problems are not always well-defined upfront<br><br></li> </ul> <h4><strong>Nice to Have</strong></h4> <ul> <li>Experience supporting <strong>mission-critical internal platforms</strong><strong><br><br></strong></li> <li>Exposure to research or experimentation-heavy environments<br><br></li> <li>Familiarity working alongside researchers or highly specialized domain experts<br><br></li> </ul> <h4><strong>Explicitly Not Required</strong></h4> <ul> <li>AI, ML, or deep learning experience<br><br></li> <li>Model training, tuning, or ML framework expertise (e.g. PyTorch, JAX)<br><br></li> </ul> <h4><strong>Ways This Role May Not Be a Fit</strong></h4> <p>This role may not be a good match if:</p> <ul> <li>You are only interested in maintaining existing infrastructure without contributing to what is being built<br><br></li> <li>You want to work exclusively on customer-facing product features<br><br></li> <li>You are looking to avoid on-call or production systems entirely<br><br></li> <li>You are seeking narrowly defined work with low ambiguity and limited ownership<br><br></li> <li>You want to build or train AI models yourself rather than enable the systems around them<br><br></li> </ul> <h4><strong>Why Join the AI Research Team</strong></h4> <ul> <li><strong>High Impact:</strong> Your work directly enables new AI-powered capabilities that reach customers<br><br></li> <li><strong>High Agency:</strong> You’ll help shape what gets built, how it’s built, and whether it’s worth building<br><br></li> <li><strong>Strong Peers:</strong> Collaborate with experienced SREs, engineers, and PhD researchers<br><br></li> <li><strong>Growth:</strong> Build expertise in research-adjacent infrastructure and platform reliability<br><br></li> <li><strong>Flexibility:</strong> Australia-based role with remote-friendly culture; occasional off-hours collaboration may be required<br><br></li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>Algolia does not discriminate on the basis of race, color, religion, sex, age, national origin, military status, veteran status, disability status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.</p> <p>The annual base salary compensation range for this role reflects market pay data within this location. The exact compensation offered for this role may vary depending on specific location and job-related knowledge, technical skills, and experience; and is only one part of our Total Rewards philosophy to compensate and recognize employees for their work.</p></div><div class="title">Base Salary Pay Range</div><div class="pay-range"><span>$152,000</span><span class="divider">&mdash;</span><span>$178,200 AUD</span></div></div></div><div class="content-conclusion"><hr> <p><strong>FLEXIBLE WORKPLACE STRATEGY:</strong></p> <p>Algolia’s flexible workplace model is designed to empower all Algolians to fulfill our mission to power search and discovery with ease. We place an emphasis on an individual’s impact, contribution, and output, over their physical location. Algolia is a high-trust environment and many of our team members hav