Senior Principal Machine Learning Systems Engineer, Search Platform

Atlassian is a software company whose Search Platform team powers Rovo Search and knowledge discovery across enterprise data. The Senior Principal Machine Learning Systems Engineer will lead the technical direction for Search Serving, building scalable agentic search systems and advancing machine learning inference, retrieval, ranking, quality, reliability, and cost efficiency.

Responsibilities:

• Design and build high quality agentic search systems, addressing tail latency, throughput, concurrency, caching, sharding, fault isolation, capacity, and graceful degradation
• Set the long-term architecture and technical roadmap for online retrieval, index serving, query processing, and ML inference. Turn advances in information retrieval and machine learning into production systems through rigorous evaluation, experimentation, and incremental adoption
• Establish strong ML production practices spanning offline and online evaluation, experimentation, observability, safe rollout and rollback, model-quality regressions, and incident response
• Advance production inference for embedding, retrieval, and ranking models through techniques such as batching, quantization, distillation, compilation, and hardware-aware optimization
• Lead step-change initiatives across multiple teams, simplify fragmented systems, mentor senior engineers, and align technical and product leaders around long-term direction. Uplevel team and platform maturity to best-in-class agentic development practices to accelerate innovation

Requirements:

• 10+ years of relevant experience, including designing and operating large-scale, low-latency search, recommendation, advertising, or retrieval systems
• Deep ML systems experience, including production inference, model optimization, retrieval and ranking pipelines, evaluation, experimentation, and operations
• Strong distributed-systems expertise across performance, concurrency, availability, multi-tenancy, capacity planning, observability, and cost efficiency
• Strong information-retrieval and applied-ML foundations, with practical experience across lexical, semantic, vector, or hybrid retrieval
• A track record of setting cross-organization technical direction, resolving consequential architecture choices, and delivering through multiple teams and senior technical leaders
• Strong product and technical judgment: framing important problems, testing hypotheses, comparing alternatives, and stopping investments whose value does not justify their complexity