Senior Machine Learning System Engineer - Agentic Search & Intelligence

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.

The way people find and use knowledge at work is changing.

Search is no longer just about returning ten relevant results.

AI agents need to understand what a user is trying to accomplish, decide where and how to search, reason across multiple sources, select the right tools and procedures, identify missing information, and assemble trustworthy context before taking action.

We are building the intelligence layer that makes this possible across Atlassian and the broader enterprise ecosystem through context-layer.

Our team works at the intersection of information retrieval, machine learning, LLMs, agentic systems, and large-scale distributed systems.

We are solving problems ranging from state-of-the-art retrieval and ranking to multi-step search, tool intelligence, context synthesis, and agent-facing search infrastructure.

If you are excited about defining what search looks like in the agent era, this is an opportunity to work on problems where many of the best solutions have yet to be invented.

What you’ll work on You will help build systems that enable AI agents to discover information, reason over enterprise knowledge, and choose the right capabilities to complete complex tasks.

Depending on your interests and expertise, you may work across areas such as:

Agentic Search — Build systems that plan, decompose, reformulate, and iteratively execute searches across multiple steps and data sources.

Agent Search Experiences — Make search agent-friendly, define the interfaces, APIs, contracts, result formats, citations, and search primitives through which agents interact with enterprise knowledge.

Retrieval & Relevance — Advance enterprise retrieval through embeddings, hybrid retrieval, learning-to-rank, LLM reranking, query understanding, and large-scale relevance optimization.

Procedural Intelligence — Teach agents to understand what tools and procedures are available, what they can do, and which capability should be used for a particular task.

Evaluation & Learning — Develop offline and online evaluation systems for retrieval quality, agent behavior, tool selection, grounding, and end-to-end task success.