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About the Role
As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search experiences across Atlassian's product suite, including Jira, Confluence, and Rovo.
Responsibilities
- Design and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search.
- Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants.
- Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality.
- Build and maintain production ML models including neural rankers, embedding models, and reranking systems.
- Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency.
- Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.
- Design retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows.
- Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.
- Drive observability, monitoring, and incident response for search serving systems.
- Apply FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets.
- Maintain production health through rigorous on-call practices, runbook development, and proactive capacity planning.
- Work closely with engineering leads, product managers, and platform stakeholders to define technical roadmaps and deliver against team OKRs.
- Mentor junior engineers, contribute to design reviews, and champion engineering best practices across the team.
Requirements
- Experience with Triton and PyTorch frameworks.
- Experience with FinOps principles.
- Experience with on-call practices, runbook development, and capacity planning.
- Experience mentoring junior engineers.
Skills
- Machine Learning Systems Engineering
- Search Platform Engineering
- Scalable search serving infrastructure
- Retrieval pipelines
- Vector indexing systems
- Embedding-based semantic search
- ML model development
- ML model serving
- Neural rankers
- Embedding models
- Reranking systems
- Agentic Search
- Retrieval-Augmented Generation (RAG)
- Personalized indexes
- Grounding pipelines
- Multi-step retrieval workflows
- Observability
- Monitoring
- Incident response
- Cost optimization
- Capacity planning
- Technical roadmaps
- Engineering best practices
Work Type
- Office
- Home
- Combination of office and home
Experience Level
- Senior
Salary/Compensations
- Zone A: $180,000 - $235,000
- Zone B: $162,000 - $211,500
- Zone C: $149,400 - $195,050
Benefits
- Health and wellbeing resources
- Paid volunteer days
About the Company
- Atlassian's 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.
Equal Opportunity
- 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.
- We can support with accommodations or adjustments at any stage of the recruitment process.
- Identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.