Job description

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. These problems operate at enterprise scale, where permissions, freshness, latency, trust, heterogeneous data, and multi-tenant systems all matter. What we’re looking for We care more about technical depth, judgment, and demonstrated impact than a specific background. Strong candidates will have several of the following: • Deep experience in machine learning, information retrieval, recommendation, ranking, NLP, LLMs, or adjacent areas. • Experience building ML systems that operate at meaningful production scale. • Strong software engineering fundamentals and the ability to turn modeling ideas into reliable production systems. • Experience designing experiments and evaluations for complex ML systems. • Ability to reason from first principles about ambiguous problems rather than simply applying existing techniques. • A track record of driving meaningful technical or product outcomes. • Strong technical communication and the ability to influence engineers and leaders across teams. Experience in any/some of the following is especially relevant: • Agentic systems, multi-step reasoning, search agents, or LLM tool use • Retrieval-augmented generation and context engineering • Tool discovery, tool ranking, MCP, or capability routing • Evidence aggregation, grounding, citations, or hallucination reduction Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes. At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. This role may also be eligible for benefits, bonuses, commissions, and equity.

Requirements

Must-haves

Working at Atlassian
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.
These problems operate at enterprise scale, where permissions, freshness, latency, trust, heterogeneous data, and multi-tenant systems all matter.
What we’re looking for
We care more about technical depth, judgment, and demonstrated impact than a specific background.
Strong candidates will have several of the following:
Deep experience in machine learning, information retrieval, recommendation, ranking, NLP, LLMs, or adjacent areas.
Experience building ML systems that operate at meaningful production scale.
Strong software engineering fundamentals and the ability to turn modeling ideas into reliable production systems.
Experience designing experiments and evaluations for complex ML systems.
Ability to reason from first principles about ambiguous problems rather than simply applying existing techniques.
A track record of driving meaningful technical or product outcomes.
Strong technical communication and the ability to influence engineers and leaders across teams.
Experience in any/some of the following is especially relevant:
Agentic systems, multi-step reasoning, search agents, or LLM tool use
Retrieval-augmented generation and context engineering

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningllmmlgo

Platform: atlassian
Effort: medium