Senior Principal Machine Learning Systems Engineer, Search Platform
Atlassian•Austin, TX
•$226,341 - $356,025•Remote
About The Position
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.
Requirements
- 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
- Experience with enterprise search, multi-tenant serving, compliance-constrained environments (FedRAMp, isolated cloud), or cloud-native ML on GCP/AWS.
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
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
