Principal Machine Learning Architect | Enterprise Agentic Search

Atlassian, Seattle, WA

The role

Overview

What you need

Substantial hands-on industry experience architecting, delivering, and evolving production RAG or agentic search systems used by real customers, with particular depth in enterprise applications.
A record of owning architecture from ambiguous customer needs through system design, implementation, launch, and evolution, with measurable improvements in search quality, task success, or customer outcomes.
Demonstrated ability to define system boundaries, interfaces, and shared capabilities across teams, and to make sound architectural decisions about scalability, reliability, extensibility, and operational complexity.
Practical experience with enterprise knowledge and workflows, including heterogeneous data sources, access permissions, freshness, domain-specific terminology, and complex information needs spanning multiple systems.

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 accountability for architectural outcomes and measurable improvements in agent task success.

Principal Machine Learning Architect | Enterprise Agentic Search

The role

What you need

Principal Machine Learning Architect | Enterprise Agentic Search

The role

What you need

Working at Atlassian

You will be working on some of the following areas

Pay and benefits

Compensation

Pay Ranges

Benefits & Perks

About Atlassian

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