Senior Machine Learning System Engineer - Agentic Search & Intelligence
About The Position
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. 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.
Requirements
- 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.
Nice To Haves
- 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
Responsibilities
- Build systems that plan, decompose, reformulate, and iteratively execute searches across multiple steps and data sources.
- Make search agent-friendly, define the interfaces, APIs, contracts, result formats, citations, and search primitives through which agents interact with enterprise knowledge.
- Advance enterprise retrieval through embeddings, hybrid retrieval, learning-to-rank, LLM reranking, query understanding, and large-scale relevance optimization.
- Teach agents to understand what tools and procedures are available, what they can do, and which capability should be used for a particular task.
- Develop offline and online evaluation systems for retrieval quality, agent behavior, tool selection, grounding, and end-to-end task success.
Benefits
- health and wellbeing resources
- paid volunteer days
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
