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. Search Platform’s mission is to power world-class, trusted cross-product knowledge search and discovery for people and agents across all of Atlassian’s surfaces. Agentic search is pushing the boundaries of what is possible with today’s search systems and this role is intended to redefine context search and discovery for AI. You'll lead the search and ML architecture that powers search quality, latency, cost, and reliability at scale and set the technical direction for the department. Responsibilities • Set technical direction for ML-based search serving: ranking models, retrieval architectures, inference pipelines, and serving infrastructure • Drive measurable improvements across search quality, latency, serving cost, and system reliability through rigorous experimentation and principled engineering • Identify and pursue moonshots — high-ambition, calculated bets on search techniques that deliver step-change improvements • Lead model optimisation end-to-end : quantisation, distillation, batching strategies, hardware-aware inference, and latency/accuracy trade-offs • Define and enforce ML systems standards : model evaluation, shadow traffic testing, rollout safety, and production observability • Mentor and elevate senior engineers across Search Serving; raise the technical bar through design reviews, architecture decisions, and hands-on guidance • Partner across teams - Search Quality, ML Platform, and product - to align roadmaps and unblock high-impact work • Translate ambiguous problems into clear technical bets with measurable success criteria Qualifications • 12+ years of engineering experience, with significant depth in ML systems and production ML infrastructure • Proven track record designing and shipping low-latency ML serving systems at scale (sub-100ms ranking, retrieval, or inference pipelines) • Deep expertise in information retrieval and search : dense/sparse retrieval, neural reranking, hybrid search, learning-to-rank • Strong command of model optimisation techniques and experience with large-scale serving infrastructure : model serving frameworks (Triton, TorchServe, vLLM or equivalent), GPU/CPU optimisation, autoscaling • Track record of technical leadership without authority - influencing architecture and decisions across team and org boundaries • Demonstrated ability to identify high-leverage research directions and drive them from prototype to production • Experience with online experimentation and rigorous evaluation frameworks for ML systems • Strong communication skills - able to distill complex trade-offs into crisp decisions for technical and non-technical audiences Bonus: Experience with enterprise search, multi-tenant serving, compliance-constrained environments (FedRAMP, isolated cloud), or cloud-native ML on GCP/AWS. 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
Set technical direction for ML-based search serving: ranking models, retrieval architectures, inference pipelines, and serving infrastructure
Drive measurable improvements across search quality, latency, serving cost, and system reliability through rigorous experimentation and principled engineering
Identify and pursue moonshots — high-ambition, calculated bets on search techniques that deliver step-change improvements
Lead model optimisation end-to-end : quantisation, distillation, batching strategies, hardware-aware inference, and latency/accuracy trade-offs
Define and enforce ML systems standards : model evaluation, shadow traffic testing, rollout safety, and production observability
Mentor and elevate senior engineers across Search Serving; raise the technical bar through design reviews, architecture decisions, and hands-on guidance
Partner across teams - Search Quality, ML Platform, and product - to align roadmaps and unblock high-impact work
Translate ambiguous problems into clear technical bets with measurable success criteria
12+ years of engineering experience, with significant depth in ML systems and production ML infrastructure
Proven track record designing and shipping low-latency ML serving systems at scale (sub-100ms ranking, retrieval, or inference pipelines)
Deep expertise in information retrieval and search : dense/sparse retrieval, neural reranking, hybrid search, learning-to-rank
Strong command of model optimisation techniques and experience with large-scale serving infrastructure : model serving frameworks (Triton, TorchServe, vLLM or equivalent), GPU/CPU optimisation, autoscaling
Track record of technical leadership without authority - influencing architecture and decisions across team and org boundaries
Demonstrated ability to identify high-leverage research directions and drive them from prototype to production
Experience with online experimentation and rigorous evaluation frameworks for ML systems
Strong communication skills - able to distill complex trade-offs into crisp decisions for technical and non-technical audiences

Nice-to-haves

Bonus: Experience with enterprise search, multi-tenant serving, compliance-constrained environments (FedRAMP, isolated cloud), or cloud-native ML on GCP/AWS.

Application prep

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

Keywords to mirror

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Platform: atlassian
Effort: medium