Principle Machine Learning Architect | Enterprise Agentic Search

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. Your future team Rovo helps people and AI agents find, understand, and act on enterprise knowledge. We build the search and machine-learning capabilities that connect agents with relevant information across Atlassian products and connected applications. Enterprise search presents a distinctive challenge for agents. Information is scattered across documents, conversations, tickets, and other systems. Questions depend on company-specific terminology, relationships, permissions, and information that changes over time. Agents need to discover the right sources, refine their searches as they learn, and gather enough reliable evidence to complete a task efficiently. We are hiring a Principal Architect to define how enterprise search powers AI agents. You will own some of the followings : the architecture and technical direction connecting retrieval, agent orchestration, context construction, evaluation, and large language model (LLM) training. Your work will help agents search effectively, gather reliable evidence, and reason over enterprise knowledge across products and applications. This is a hands-on architect role with impact across multiple teams. You will make foundational design decisions, validate them through prototypes and production evidence, and guide engineers and scientists through implementation and adoption. You will establish a coherent architecture that teams can evolve as models, enterprise data, and customer needs change. You will be working on some of the following areas: Own the architecture for enterprise agentic search. Define how query understanding, source discovery, retrieval, agent orchestration, tool use, and context construction work together. Establish system boundaries, interfaces, and reusable capabilities that support multiple agent experiences. Design for enterprise complexity. Make architectural decisions for heterogeneous content, permissions, tenant isolation, freshness, domain-specific language, and information needs spanning multiple systems. Balance search quality and agent effectiveness with scalability, latency, cost, and reliability. Architect evaluation and experimentation. Define the benchmarks, datasets, evaluation environments, and observability needed to assess retrieval quality, agent search behavior, evidence coverage, and task success. Guide teams in building reproducible experiments and calibrated evaluators that inform architecture, model, and launch decisions. Set the direction for training LLMs for agentic search. Define model capabilities, learning objectives, training data requirements, and the architecture connecting training, evaluation, and serving. Lead technical decisions on supervised fine-tuning, preference optimization, and reinforcement learning to improve search planning, tool use, iterative evidence gathering, and grounded reasoning. Partner with engineers and scientists to train, validate, and deploy these improvements. Design the continuous improvement loop. Connect production interactions, agent trajectories, and failure analysis to evaluation cases, training data, and model updates. Establish data and feedback interfaces that preserve permissions, privacy, and evaluation integrity. Validate architecture through implementation. Build prototypes and reference implementations, investigate difficult system failures, and work directly with teams on critical components. Use experiments and production results to resolve trade-offs and guide incremental adoption. Lead technical direction across teams. Set a multi-quarter roadmap, align search, agent, and ML platform teams on shared architecture, and guide implementation through design reviews and technical mentorship. Take account