Senior Principal Machine Learning Systems Engineer, Search Platform
About The Position
The Search Platform team is responsible for powering Rovo Search, knowledge discovery and search for humans and agents across all enterprise data. Our mission is to make all our customers' knowledge across Atlassian and their external apps (i.e. third-party applications) searchable securely and reliably through an insanely fast, globally scalable, cost-efficient, enterprise grade search infrastructure. Powering search and discovery in the agentic era is a whole new challenge that you will help lead. As the technical lead for Search Serving, you will set its long-term technical direction and be accountable for its contribution to end-to-end search quality, tail latency, reliability, scalability, operability, and cost. You will work with Relevance, Search Data, and Product to determine where new ML capabilities can meaningfully change search experience, quality and platform capabilties.
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.
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.
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
