Senior Machine Learning System Engineer

Atlassian develops software products that help teams collaborate and work effectively. The Senior Machine Learning System Engineer will design, develop, and deploy machine learning systems powering search experiences across Atlassian products, including Jira, Confluence, and Rovo. The role focuses on search infrastructure, ML model serving, agentic retrieval, operational excellence, cost optimization, and cross-functional engineering leadership.

Responsibilities:

• Design and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search
• Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants
• Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality
• Build and maintain production ML models including neural rankers, embedding models, and reranking systems
• Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency
• Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses
• Design retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows
• Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale
• Drive observability, monitoring, and incident response for search serving systems
• Apply FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets
• Maintain production health through rigorous on-call practices, runbook development, and proactive capacity planning
• Work closely with engineering leads, product managers, and platform stakeholders to define technical roadmaps and deliver against team OKRs
• Mentor junior engineers, contribute to design reviews, and champion engineering best practices across the team