Senior Machine Learning System Engineer

Overview: Working at Atlassian

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.

Responsibilities

As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search experiences across Atlassian's product suite, including Jira, Confluence, and Rovo.

Search Platform Engineering

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.

ML Model Development & Serving

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.

Agentic Search & Retrieval

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.

Operational Excellence & Cost Discipline

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