Job description

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: Search Platform 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. 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. Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes. At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. This role may also be eligible for benefits, bonuses, commissions, and equity.

Requirements

Must-haves

Working at Atlassian
Your future team: Search Platform
Design and build high quality agentic search systems, addressing tail latency, throughput, concurrency, caching, sharding, fault isolation, capacity, and graceful degradation.
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learninggo

Platform: atlassian
Effort: medium